Search results for: satellite images
2710 Improving Temporal Correlations in Empirical Orthogonal Function Expansions for Data Interpolating Empirical Orthogonal Function Algorithm
Authors: Ping Bo, Meng Yunshan
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Satellite-derived sea surface temperature (SST) is a key parameter for many operational and scientific applications. However, the disadvantage of SST data is a high percentage of missing data which is mainly caused by cloud coverage. Data Interpolating Empirical Orthogonal Function (DINEOF) algorithm is an EOF-based technique for reconstructing the missing data and has been widely used in oceanographic field. The reconstruction of SST images within a long time series using DINEOF can cause large discontinuities and one solution for this problem is to filter the temporal covariance matrix to reduce the spurious variability. Based on the previous researches, an algorithm is presented in this paper to improve the temporal correlations in EOF expansion. Similar with the previous researches, a filter, such as Laplacian filter, is implemented on the temporal covariance matrix, but the temporal relationship between two consecutive images which is used in the filter is considered in the presented algorithm, for example, two images in the same season are more likely correlated than those in the different seasons, hence the latter one is less weighted in the filter. The presented approach is tested for the monthly nighttime 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST for the long-term period spanning from 1989 to 2006. The results obtained from the presented algorithm are compared to those from the original DINEOF algorithm without filtering and from the DINEOF algorithm with filtering but without taking temporal relationship into account.Keywords: data interpolating empirical orthogonal function, image reconstruction, sea surface temperature, temporal filter
Procedia PDF Downloads 3202709 Level Set and Morphological Operation Techniques in Application of Dental Image Segmentation
Authors: Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, Alireza Norouzi
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Medical image analysis is one of the great effects of computer image processing. There are several processes to analysis the medical images which the segmentation process is one of the challenging and most important step. In this paper the segmentation method proposed in order to segment the dental radiograph images. Thresholding method has been applied to simplify the images and to morphologically open binary image technique performed to eliminate the unnecessary regions on images. Furthermore, horizontal and vertical integral projection techniques used to extract the each individual tooth from radiograph images. Segmentation process has been done by applying the level set method on each extracted images. Nevertheless, the experiments results by 90% accuracy demonstrate that proposed method achieves high accuracy and promising result.Keywords: integral production, level set method, morphological operation, segmentation
Procedia PDF Downloads 3112708 Hydrological Evaluation of Satellite Precipitation Products Using IHACRES Rainfall-Runoff Model over a Basin in Iran
Authors: Mahmoud Zakeri Niri, Saber Moazami, Arman Abdollahipour, Hossein Ghalkhani
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The objective of this research is to hydrological evaluation of four widely-used satellite precipitation products named PERSIANN, TMPA-3B42V7, TMPA-3B42RT, and CMORPH over Zarinehrood basin in Iran. For this aim, at first, daily streamflow of Sarough-cahy river of Zarinehrood basin was simulated using IHACRES rainfall-runoff model with daily rain gauge and temperature as input data from 1988 to 2008. Then, the model was calibrated in two different periods through comparison the simulated discharge with the observed one at hydrometric stations. Moreover, in order to evaluate the performance of satellite precipitation products in streamflow simulation, the calibrated model was validated using daily satellite rainfall estimates from the period of 2003 to 2008. The obtained results indicated that TMPA-3B42V7 with CC of 0.69, RMSE of 5.93 mm/day, MAE of 4.76 mm/day, and RBias of -5.39% performs better simulation of streamflow than those PERSIANN and CMORPH over the study area. It is noteworthy that in Iran, the availability of ground measuring station data is very limited because of the sparse density of hydro-meteorological networks. On the other hand, large spatial and temporal variability of precipitations and lack of a reliable and extensive observing system are the most important challenges to rainfall analysis, flood prediction, and other hydrological applications in this country.Keywords: hydrological evaluation, IHACRES, satellite precipitation product, streamflow simulation
Procedia PDF Downloads 2332707 Urban Heat Islands Analysis of Matera, Italy Based on the Change of Land Cover Using Satellite Landsat Images from 2000 to 2017
Authors: Giuseppina Anna Giorgio, Angela Lorusso, Maria Ragosta, Vito Telesca
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Climate change is a major public health threat due to the effects of extreme weather events on human health and on quality of life in general. In this context, mean temperatures are increasing, in particular, extreme temperatures, with heat waves becoming more frequent, more intense, and longer lasting. In many cities, extreme heat waves have drastically increased, giving rise to so-called Urban Heat Island (UHI) phenomenon. In an urban centre, maximum temperatures may be up to 10° C warmer, due to different local atmospheric conditions. UHI occurs in the metropolitan areas as function of the population size and density of a city. It consists of a significant difference in temperature compared to the rural/suburban areas. Increasing industrialization and urbanization have increased this phenomenon and it has recently also been detected in small cities. Weather conditions and land use are one of the key parameters in the formation of UHI. In particular surface urban heat island is directly related to temperatures, to land surface types and surface modifications. The present study concern a UHI analysis of Matera city (Italy) based on the analysis of temperature, change in land use and land cover, using Corine Land Cover maps and satellite Landsat images. Matera, located in Southern Italy, has a typical Mediterranean climate with mild winters and hot and humid summers. Moreover, Matera has been awarded the international title of the 2019 European Capital of Culture. Matera represents a significant example of vernacular architecture. The structure of the city is articulated by a vertical succession of dug layers sometimes excavated or partly excavated and partly built, according to the original shape and height of the calcarenitic slope. In this study, two meteorological stations were selected: MTA (MaTera Alsia, in industrial zone) and MTCP (MaTera Civil Protection, suburban area located in a green zone). In order to evaluate the increase in temperatures (in terms of UHI occurrences) over time, and evaluating the effect of land use on weather conditions, the climate variability of temperatures for both stations was explored. Results show that UHI phenomena is growing in Matera city, with an increase of maximum temperature values at a local scale. Subsequently, spatial analysis was conducted by Landsat satellite images. Four years was selected in the summer period (27/08/2000, 27/07/2006, 11/07/2012, 02/08/2017). In Particular, Landsat 7 ETM+ for 2000, 2006 and 2012 years; Landsat 8 OLI/TIRS for 2017. In order to estimate the LST, Mono Window Algorithm was applied. Therefore, the increase of LST values spatial scale trend has been verified, in according to results obtained at local scale. Finally, the analysis of land use maps over the years by the LST and/or the maximum temperatures measured, show that the development of industrialized area produces a corresponding increase in temperatures and consequently a growth in UHI.Keywords: climate variability, land surface temperature, LANDSAT images, urban heat island
Procedia PDF Downloads 1182706 An Algorithm for Removal of Noise from X-Ray Images
Authors: Sajidullah Khan, Najeeb Ullah, Wang Yin Chai, Chai Soo See
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In this paper, we propose an approach to remove impulse and Poisson noise from X-ray images. Many filters have been used for impulse noise removal from color and gray scale images with their own strengths and weaknesses but X-ray images contain Poisson noise and unfortunately there is no intelligent filter which can detect impulse and Poisson noise from X-ray images. Our proposed filter uses the upgraded layer discrimination approach to detect both Impulse and Poisson noise corrupted pixels in X-ray images and then restores only those detected pixels with a simple efficient and reliable one line equation. Our Proposed algorithms are very effective and much more efficient than all existing filters used only for Impulse noise removal. The proposed method uses a new powerful and efficient noise detection method to determine whether the pixel under observation is corrupted or noise free. Results from computer simulations are used to demonstrate pleasing performance of our proposed method.Keywords: X-ray image de-noising, impulse noise, poisson noise, PRWF
Procedia PDF Downloads 3742705 Hyperspectral Imagery for Tree Speciation and Carbon Mass Estimates
Authors: Jennifer Buz, Alvin Spivey
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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation
Procedia PDF Downloads 1152704 Implementation of Achterbahn-128 for Images Encryption and Decryption
Authors: Aissa Belmeguenai, Khaled Mansouri
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In this work, an efficient implementation of Achterbahn-128 for images encryption and decryption was introduced. The implementation for this simulated project is written by MATLAB.7.5. At first two different original images are used for validate the proposed design. Then our developed program was used to transform the original images data into image digits file. Finally, we used our implemented program to encrypt and decrypt images data. Several tests are done for proving the design performance including visual tests and security analysis; we discuss the security analysis of the proposed image encryption scheme including some important ones like key sensitivity analysis, key space analysis, and statistical attacks.Keywords: Achterbahn-128, stream cipher, image encryption, security analysis
Procedia PDF Downloads 5242703 Thermal Vacuum Chamber Test Result for CubeSat Transmitter
Authors: Fitri D. Jaswar, Tharek A. Rahman, Yasser A. Ahmad
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CubeSat in low earth orbit (LEO) mainly uses ultra high frequency (UHF) transmitter with fixed radio frequency (RF) output power to download the telemetry and the payload data. The transmitter consumes large amount of electrical energy during the transmission considering the limited satellite size of a CubeSat. A transmitter with power control ability is designed to achieve optimize the signal to noise ratio (SNR) and efficient power consumption. In this paper, the thermal vacuum chamber (TVAC) test is performed to validate the performance of the UHF band transmitter with power control capability. The TVAC is used to simulate the satellite condition in the outer space environment. The TVAC test was conducted at the Laboratory of Spacecraft Environment Interaction Engineering, Kyushu Institute of Technology, Japan. The TVAC test used 4 thermal cycles starting from +60°C to -20°C for the temperature setting. The pressure condition inside chamber was less than 10-5Pa. During the test, the UHF transmitter is integrated in a CubeSat configuration with other CubeSat subsystem such as on board computer (OBC), power module, and satellite structure. The system is validated and verified through its performance in terms of its frequency stability and the RF output power. The UHF band transmitter output power is tested from 0.5W to 2W according the satellite mode of operations and the satellite power limitations. The frequency stability is measured and the performance obtained is less than 2 ppm in the tested operating temperature range. The test demonstrates the RF output power is adjustable in a thermal vacuum condition.Keywords: communication system, CubeSat, SNR, UHF transmitter
Procedia PDF Downloads 2572702 Classification of Computer Generated Images from Photographic Images Using Convolutional Neural Networks
Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia
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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks
Procedia PDF Downloads 3282701 Crater Pattern on the Moon and Origin of the Moon
Authors: Xuguang Leng
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The crater pattern on the Moon indicates the Moon was captured by Earth in the more recent years, disproves the theory that the Moon was born as a satellite to the Earth. The Moon was tidal locked since it became the satellite of the Earth. Moon’s near side is shielded by Earth from asteroid/comet collisions, with the center of the near side most protected. Yet the crater pattern on the Moon is fairly random, with no distinguishable empty spot/strip, no distinguishable difference near side vs. far side. Were the Moon born as Earth’s satellite, there would be a clear crater free spot, or strip should the tial lock shifts over time, on the near side; and far more craters on the far side. The nonexistence of even a vague crater free spot on the near side of the Moon indicates the capture was a more recent event. Given Earth’s much larger mass and sphere size over the Moon, Earth should have collided with asteroids and comets in much higher frequency, resulting in significant mass gain over the lifespan. Earth’s larger mass and magnetic field are better at retaining water and gas from solar wind’s stripping effect, thus accelerating the mass gain. A dwarf planet Moon can be pulled closer and closer to the Earth over time as Earth’s gravity grows stronger, eventually being captured as a satellite. Given enough time, it is possible Earth’s mass would be large enough to cause the Moon to collide with Earth.Keywords: moon, origin, crater, pattern
Procedia PDF Downloads 922700 Digital Mapping of First-Order Drainages and Springs of the Guajiru River, Northeast of Brazil, Based on Satellite and Drone Images
Authors: Sebastião Milton Pinheiro da Silva, Michele Barbosa da Rocha, Ana Lúcia Fernandes Campos, Miquéias Rildo de Souza Silva
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Water is an essential natural resource for life on Earth. Rivers, lakes, lagoons and dams are the main sources of water storage for human consumption. The costs of extracting and using these water sources are lower than those of exploiting groundwater on transition zones to semi-arid terrains. However, the volume of surface water has decreased over time, with the depletion of first-order drainage and the disappearance of springs, phenomena which are easily observed in the field. Climate change worsens water scarcity, compromising supply and hydric security for rural populations. To minimize the expected impacts, producing and storing water through watershed management planning requires detailed cartographic information on the relief and topography, and updated data on the stage and intensity of catchment basin environmental degradation problems. The cartography available of the Brazilian northeastern territory dates to the 70s, with topographic maps, printed, at a scale of 1:100,000 which does not meet the requirements to execute this project. Exceptionally, there are topographic maps at scales of 1:50,000 and 1:25,000 of some coastal regions in northeastern Brazil. Still, due to scale limitations and outdatedness, they are products of little utility for mapping low-order watersheds drainage and springs. Remote sensing data and geographic information systems can contribute to guiding the process of mapping and environmental recovery by integrating detailed relief and topographic data besides social and other environmental information in the Guajiru River Basin, located on the east coast of Rio Grande do Norte, on the Northeast region of Brazil. This study aimed to recognize and map catchment basin, springs and low-order drainage features along estimating morphometric parameters. Alos PALSAR and Copernicus DEM digital elevation models were evaluated and provided regional drainage features and the watersheds limits extracted with Terraview/Terrahidro 5.0 software. CBERS 4A satellite images with 2 m spatial resolution, processed with ESA SNAP Toolbox, allowed generating land use land cover map of Guajiru River. A Mappir Survey 3 multiespectral camera onboard of a DJI Phantom 4, a Mavic 2 Pro PPK Drone and an X91 GNSS receiver to collect the precised position of selected points were employed to detail mapping. Satellite images enabled a first knowledge approach of watershed areas on a more regional scale, yet very current, and drone images were essential in mapping details of catchment basins. The drone multispectral image mosaics, the digital elevation model, the contour lines and geomorphometric parameters were generated using OpenDroneMap/ODM and QGis softwares. The drone images generated facilitated the location, understanding and mapping of watersheds, recharge areas and first-order ephemeral watercourses on an adequate scale and will be used in the following project’s phases: watershed management planning, recovery and environmental protection of Rio's springs Guajiru. Environmental degradation is being analyzed from the perspective of the availability and quality of surface water supply.Keywords: imaging, relief, UAV, water
Procedia PDF Downloads 182699 Improved Multi-Channel Separation Algorithm for Satellite-Based Automatic Identification System Signals Based on Artificial Bee Colony and Adaptive Moment Estimation
Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo
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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation
Procedia PDF Downloads 1812698 Rigorous Photogrammetric Push-Broom Sensor Modeling for Lunar and Planetary Image Processing
Authors: Ahmed Elaksher, Islam Omar
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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition
Procedia PDF Downloads 602697 Clustering Based Level Set Evaluation for Low Contrast Images
Authors: Bikshalu Kalagadda, Srikanth Rangu
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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization
Procedia PDF Downloads 3442696 Comparative Analysis of Dissimilarity Detection between Binary Images Based on Equivalency and Non-Equivalency of Image Inversion
Authors: Adnan A. Y. Mustafa
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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping
Procedia PDF Downloads 1482695 Objective Evaluation on Medical Image Compression Using Wavelet Transformation
Authors: Amhimmid Mohammed Saffour, Mustafa Mohamed Abdullah
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The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable.Keywords: medical image, Matlab, image compression, wavelet's, objective evaluation
Procedia PDF Downloads 2822694 Comparative Study of Different Enhancement Techniques for Computed Tomography Images
Authors: C. G. Jinimole, A. Harsha
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One of the key problems facing in the analysis of Computed Tomography (CT) images is the poor contrast of the images. Image enhancement can be used to improve the visual clarity and quality of the images or to provide a better transformation representation for further processing. Contrast enhancement of images is one of the acceptable methods used for image enhancement in various applications in the medical field. This will be helpful to visualize and extract details of brain infarctions, tumors, and cancers from the CT image. This paper presents a comparison study of five contrast enhancement techniques suitable for the contrast enhancement of CT images. The types of techniques include Power Law Transformation, Logarithmic Transformation, Histogram Equalization, Contrast Stretching, and Laplacian Transformation. All these techniques are compared with each other to find out which enhancement provides better contrast of CT image. For the comparison of the techniques, the parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used. Logarithmic Transformation provided the clearer and best quality image compared to all other techniques studied and has got the highest value of PSNR. Comparison concludes with better approach for its future research especially for mapping abnormalities from CT images resulting from Brain Injuries.Keywords: computed tomography, enhancement techniques, increasing contrast, PSNR and MSE
Procedia PDF Downloads 3032693 Collocation Assessment between GEO and GSO Satellites
Authors: A. E. Emam, M. Abd Elghany
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The change in orbit evolution between collocated satellites (X, Y) inside +/-0.09 ° E/W and +/- 0.07 ° N/S cluster, after one of these satellites is placed in an inclined orbit (satellite X) and the effect of this change in the collocation safety inside the cluster window has been studied and evaluated. Several collocation scenarios had been studied in order to adjust the location of both satellites inside their cluster to maximize the separation between them and safe the mission.Keywords: satellite, GEO, collocation, risk assessment
Procedia PDF Downloads 3892692 Detecting the Palaeochannels Based on Optical Data and High-Resolution Radar Data for Periyarriver Basin
Authors: S. Jayalakshmi, Gayathri S., Subiksa V., Nithyasri P., Agasthiya
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Paleochannels are the buried part of an active river system which was separated from the active river channel by the process of cutoff or abandonment during the dynamic evolution of the active river. Over time, they are filled by young unconsolidated or semi-consolidated sediments. Additionally, it is impacted by geo morphological influences, lineament alterations, and other factors. The primary goal of this study is to identify the paleochannels in Periyar river basin for the year 2023. Those channels has a high probability in the presence of natural resources, including gold, platinum,tin,an duranium. Numerous techniques are used to map the paleochannel. Using the optical data, Satellite images were collected from various sources, which comprises multispectral satellite images from which indices such as Normalized Difference Vegetation Index (NDVI),Normalized Difference Water Index (NDWI), Soil Adjusted Vegetative Index (SAVI) and thematic layers such as Lithology, Stream Network, Lineament were prepared. Weights are assigned to each layer based on its importance, and overlay analysis has done, which concluded that the northwest region of the area has shown some paleochannel patterns. The results were cross-verified using the results obtained using microwave data. Using Sentinel data, Synthetic Aperture Radar (SAR) Image was extracted from European Space Agency (ESA) portal, pre-processed it using SNAP 6.0. In addition to that, Polarimetric decomposition technique has incorporated to detect the paleochannels based on its scattering property. Further, Principal component analysis has done for enhanced output imagery. Results obtained from optical and microwave radar data were compared and the location of paleochannels were detected. It resulted six paleochannels in the study area out of which three paleochannels were validated with the existing data published by Department of Geology and Environmental Science, Kerala. The other three paleochannels were newly detected with the help of SAR image.Keywords: paleochannels, optical data, SAR image, SNAP
Procedia PDF Downloads 792691 Effects of Reversible Watermarking on Iris Recognition Performance
Authors: Andrew Lock, Alastair Allen
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Fragile watermarking has been proposed as a means of adding additional security or functionality to biometric systems, particularly for authentication and tamper detection. In this paper we describe an experimental study on the effect of watermarking iris images with a particular class of fragile algorithm, reversible algorithms, and the ability to correctly perform iris recognition. We investigate two scenarios, matching watermarked images to unmodified images, and matching watermarked images to watermarked images. We show that different watermarking schemes give very different results for a given capacity, highlighting the importance of investigation. At high embedding rates most algorithms cause significant reduction in recognition performance. However, in many cases, for low embedding rates, recognition accuracy is improved by the watermarking process.Keywords: biometrics, iris recognition, reversible watermarking, vision engineering
Procedia PDF Downloads 4492690 Satellite Images to Determine Levels of Fire Severity in a Native Chilean Forest: Assessing the Responses of Soil Mesofauna Diversity to a Fire Event
Authors: Carolina Morales, Ricardo Castro-Huerta, Enrique A. Mundaca
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The edaphic fauna is the main factor involved in the transformation of nutrients and soil decomposition processes. Edaphic organisms are highly sensitive to soil disturbances, which normally causes changes in the composition and abundance of such organisms. Fire is known to be a disturbing factor since it affects the physical, chemical and biological properties of the soil and the whole ecosystem. During the summer (December-March) of 2017, Chile suffered the major fire events recorded in its modern history, which affected a vast area and a number of ecosystem types. The objective of this study was first to use remote sensing satellite images and GIS (Geographic Information Systems) to assess and identify levels of fire severity in disturbed areas and to compare the responses of the soil mesofauna diversity among such areas. We identified four areas (treatments) with an ascending level of severity, namely: mild, medium, high severity, and free of fire. A non-affected patch of forest was established as a control. Three samples from each treatment were collected in the form of a soil cube (10x10x10 cm). Edaphic mesofauna was obtained from each sample through the Berlese-Tullgren funnel method. Collected specimens were quantified and identified, using the RTU (Recognisable Taxonomic Unit) criterion. Diversity was analysed using inferential statistics to compare Simpson and Shannon-Wiener indexes across treatments. As predicted, the unburned forest patch (control) exhibited higher diversity values than the treatments. Significantly higher diversity values were recorded in those treatments subjected to lower fire severity. We conclude that remote sensing zoning is an adequate tool to identify different levels of fire severity and that an edaphic mesofauna is a group of organisms that qualify as good bioindicators for monitoring soil recovery after fire events.Keywords: bioindicator, Chile, fire severity level, soil
Procedia PDF Downloads 1522689 Adaptive Motion Compensated Spatial Temporal Filter of Colonoscopy Video
Authors: Nidhal Azawi
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Colonoscopy procedure is widely used in the world to detect an abnormality. Early diagnosis can help to heal many patients. Because of the unavoidable artifacts that exist in colon images, doctors cannot detect a colon surface precisely. The purpose of this work is to improve the visual quality of colonoscopy videos to provide better information for physicians by removing some artifacts. This work complements a series of work consisting of three previously published papers. In this paper, Optic flow is used for motion compensation, and then consecutive images are aligned/registered to integrate some information to create a new image that has or reveals more information than the original one. Colon images have been classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade (LK) with an adaptive temporal mean/median filter, whereas noninformative images are treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) with adaptive temporal median images. A comparison result showed that this work achieved better results than that results in the state- of- the- art strategies for the same degraded colon images data set, which consists of 1000 images. The new proposed algorithm reduced the error alignment by about a factor of 0.3 with a 100% successfully image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it succeeded to convert the non-informative images that have very few details/no details because of the blurriness/out of focus or because of the specular highlight dominate significant amount of an image to informative images.Keywords: optic flow, colonoscopy, artifacts, spatial temporal filter
Procedia PDF Downloads 1092688 Study on Construction of 3D Topography by UAV-Based Images
Authors: Yun-Yao Chi, Chieh-Kai Tsai, Dai-Ling Li
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In this paper, a method of fast 3D topography modeling using the high-resolution camera images is studied based on the characteristics of Unmanned Aerial Vehicle (UAV) system for low altitude aerial photogrammetry and the need of three dimensional (3D) urban landscape modeling. Firstly, the existing high-resolution digital camera with special design of overlap images is designed by reconstructing and analyzing the auto-flying paths of UAVs, which improves the self-calibration function to achieve the high precision imaging by software, and further increased the resolution of the imaging system. Secondly, several-angle images including vertical images and oblique images gotten by the UAV system are used for the detail measure of urban land surfaces and the texture extraction. Finally, the aerial photography and 3D topography construction are both developed in campus of Chang-Jung University and in Guerin district area in Tainan, Taiwan, provide authentication model for construction of 3D topography based on combined UAV-based camera images from system. The results demonstrated that the UAV system for low altitude aerial photogrammetry can be used in the construction of 3D topography production, and the technology solution in this paper offers a new, fast, and technical plan for the 3D expression of the city landscape, fine modeling and visualization.Keywords: 3D, topography, UAV, images
Procedia PDF Downloads 2982687 A Customize Battery Management Approach for Satellite
Authors: Muhammad Affan, Muhammad Ilyas Raza, Muhammad Harris Hashmi
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This work is attributed to the battery management unit design of student Satellites under Pakistan National Student Satellite Program (PNSSP). The aim has been to design a customized, low-cost, efficient, reliable and less-complex battery management scheme for the Satellite. Nowadays, Lithium Ion (Li-ion) batteries have become the de-facto standard for remote applications, especially for satellites. Li-ion cells are selected for secondary storage. The design also addresses Li-ion safety requirements by monitoring, balancing and protecting cells for safe and prolonged operation. Accurate voltage measurement of individual cells was the main challenge because all the actions triggered were based on the digital voltage measurement. For this purpose, a resistive-divider network is used to maintain simplicity and cost-effectiveness. To cater the problem of insufficient i/o pins on microcontroller, fast multiplexers and de-multiplexers were used. The discrepancy inherited in the given design is the dissipation of heat due to the dissipative resistors. However, it is still considered to be the optimum adoption, considering the simple and cost-effective nature of the passive balancing technique. Furthermore, it is a completely unique solution, customized to meet specific requirements. However, there is still an option for a more advanced and expensive design.Keywords: satellite, battery module, passive balancing, dissipative
Procedia PDF Downloads 1262686 Classification of Forest Types Using Remote Sensing and Self-Organizing Maps
Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá
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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning
Procedia PDF Downloads 3582685 Autonomous Vehicle Detection and Classification in High Resolution Satellite Imagery
Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini
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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification
Procedia PDF Downloads 2262684 Estimation of Soil Nutrient Content Using Google Earth and Pleiades Satellite Imagery for Small Farms
Authors: Lucas Barbosa Da Silva, Jun Okamoto Jr.
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Precision Agriculture has long being benefited from crop fields’ aerial imagery. This important tool has allowed identifying patterns in crop fields, generating useful information to the production management. Reflectance intensity data in different ranges from the electromagnetic spectrum may indicate presence or absence of nutrients in the soil of an area. Different relations between the different light bands may generate even more detailed information. The knowledge of the nutrients content in the soil or in the crop during its growth is a valuable asset to the farmer that seeks to optimize its yield. However, small farmers in Brazil often lack the resources to access this kind information, and, even when they do, it is not presented in a comprehensive and/or objective way. So, the challenges of implementing this technology ranges from the sampling of the imagery, using aerial platforms, building of a mosaic with the images to cover the entire crop field, extracting the reflectance information from it and analyzing its relationship with the parameters of interest, to the display of the results in a manner that the farmer may take the necessary decisions more objectively. In this work, it’s proposed an analysis of soil nutrient contents based on image processing of satellite imagery and comparing its outtakes with commercial laboratory’s chemical analysis. Also, sources of satellite imagery are compared, to assess the feasibility of using Google Earth data in this application, and the impacts of doing so, versus the application of imagery from satellites like Landsat-8 and Pleiades. Furthermore, an algorithm for building mosaics is implemented using Google Earth imagery and finally, the possibility of using unmanned aerial vehicles is analyzed. From the data obtained, some soil parameters are estimated, namely, the content of Potassium, Phosphorus, Boron, Manganese, among others. The suitability of Google Earth Imagery for this application is verified within a reasonable margin, when compared to Pleiades Satellite imagery and to the current commercial model. It is also verified that the mosaic construction method has little or no influence on the estimation results. Variability maps are created over the covered area and the impacts of the image resolution and sample time frame are discussed, allowing easy assessments of the results. The final results show that easy and cheaper remote sensing and analysis methods are possible and feasible alternatives for the small farmer, with little access to technological and/or financial resources, to make more accurate decisions about soil nutrient management.Keywords: remote sensing, precision agriculture, mosaic, soil, nutrient content, satellite imagery, aerial imagery
Procedia PDF Downloads 1702683 Enhanced Visual Sharing Method for Medical Image Security
Authors: Kalaivani Pachiappan, Sabari Annaji, Nithya Jayakumar
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In recent years, Information security has emerged as foremost challenges in many fields. Especially in medical information systems security is a major issue, in handling reports such as patients’ diagnosis and medical images. These sensitive data require confidentiality for transmission purposes. Image sharing is a secure and fault-tolerant method for protecting digital images, which can use the cryptography techniques to reduce the information loss. In this paper, visual sharing method is proposed which embeds the patient’s details into a medical image. Then the medical image can be divided into numerous shared images and protected by various users. The original patient details and medical image can be retrieved by gathering the shared images.Keywords: information security, medical images, cryptography, visual sharing
Procedia PDF Downloads 4092682 Next-Generation Laser-Based Transponder and 3D Switch for Free Space Optics in Nanosatellite
Authors: Nadir Atayev, Mehman Hasanov
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Future spacecraft will require a structural change in the way data is transmitted due to the increase in the volume of data required for space communication. Current radio frequency communication systems are already facing a bottleneck in the volume of data sent to the ground segment due to their technological and regulatory characteristics. To overcome these issues, free space optics communication plays an important role in the integrated terrestrial space network due to its advantages such as significantly improved data rate compared to traditional RF technology, low cost, improved security, and inter-satellite free space communication, as well as uses a laser beam, which is an optical signal carrier to establish satellite-ground & ground-to-satellite links. In this approach, there is a need for high-speed and energy-efficient systems as a base platform for sending high-volume video & audio data. Nano Satellite and its branch CubeSat platforms have more technical functionality than large satellites, wheres cover an important part of the space sector, with their Low-Earth-Orbit application area with low-cost design and technical functionality for building networks using different communication topologies. Along the research theme developed in this regard, the output parameter indicators for the FSO of the optical communication transceiver subsystem on the existing CubeSat platforms, and in the direction of improving the mentioned parameters of this communication methodology, 3D optical switch and laser beam controlled optical transponder with 2U CubeSat structural subsystems and application in the Low Earth Orbit satellite network topology, as well as its functional performance and structural parameters, has been studied accordingly.Keywords: cubesat, free space optics, nano satellite, optical laser communication.
Procedia PDF Downloads 852681 Comparative Study of Greenhouse Locations through Satellite Images and Geographic Information System: Methodological Evaluation in Venezuela
Authors: Maria A. Castillo H., Andrés R. Leandro C.
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During the last decades, agricultural productivity in Latin America has increased with precision agriculture and more efficient agricultural technologies. The use of automated systems, satellite images, geographic information systems, and tools for data analysis, and artificial intelligence have contributed to making more effective strategic decisions. Twenty years ago, the state of Mérida, located in the Venezuelan Andes, reported the largest area covered by greenhouses in the country, where certified seeds of potatoes, vegetables, ornamentals, and flowers were produced for export and consumption in the central region of the country. In recent years, it is estimated that production under greenhouses has changed, and the area covered has decreased due to different factors, but there are few historical statistical data in sufficient quantity and quality to support this estimate or to be used for analysis and decision making. The objective of this study is to compare data collected about geoposition, use, and covered areas of the greenhouses in 2007 to data available in 2021, as support for the analysis of the current situation of horticultural production in the main municipalities of the state of Mérida. The document presents the development of the work in the diagnosis and integration of geographic coordinates in GIS and data analysis phases. As a result, an evaluation of the process is made, a dashboard is presented with the most relevant data along with the geographical coordinates integrated into GIS, and an analysis of the obtained information is made. Finally, some recommendations for actions are added, and works that expand the information obtained and its geographical traceability over time are proposed. This study contributes to granting greater certainty in the supporting data for the evaluation of social, environmental, and economic sustainability indicators and to make better decisions according to the sustainable development goals in the area under review. At the same time, the methodology provides improvements to the agricultural data collection process that can be extended to other study areas and crops.Keywords: greenhouses, geographic information system, protected agriculture, data analysis, Venezuela
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