Search results for: 3D remote sensing images

Authors: Ali A. Aldosari

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Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region.

Keywords: spatial analysis, geographical information system, change detection

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Authors: Bing-Bing E. Goh

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Plant counting is a labour intensive and time-consuming task for the farmers. However, it is an important indicator for farmers to make decisions on subsequent field management. This study is to evaluate the potential of Sentinel-2 images using statistical analysis to retrieve information on plant density for monitoring, especially during critical period at the beginning of March. The model was calibrated with in-situ data from 19 winter wheat fields in Republic of Ireland during the crop growing season in 2019-2020. The model for plant density resulted in R2 = 0.77, RMSECV = 103 and NRMSE = 14%. This study has shown the potential of using Sentinel-2 to estimate plant density and quantify plant establishment to effectively monitor crop progress and to ensure proper field management.

Keywords: winter wheat, remote sensing, crop monitoring, multivariate analysis

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Authors: Adnan A. Y. Mustafa

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In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented.

Keywords: big images, binary images, image matching, image similarity

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Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

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Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

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Authors: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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Authors: Tabassum Husain, Shen Peng Li, Zhaolin Chen

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This paper evaluates the usability of 3D Guided Image Filtering to enhance the quality of short-time binned dynamic PET images by using MRI images. Guided image filtering is an edge-preserving filter proposed to enhance 2D images. The 3D filter is applied on 1 and 5-minute binned images. The results are compared with 15-minute binned images and the Gaussian filtering. The guided image filter enhances the quality of dynamic PET images while also preserving important information of the voxels.

Keywords: dynamic PET images, guided image filter, image enhancement, information preservation filtering

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Satyendra Mourya, Jyoti Jaiswal, Gaurav Malik, Brijesh Kumar, Ramesh Chandra

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Present work describes the fabrication and sensing characteristics of the Pd-Pt decorated nanostructured silicon carbide (SiC) thin films on anodized porous silicon (PSi) substrate by RF magnetron sputtering. The gas sensing performance of Pd-Pt/SiC/PSi sensing electrode towards H2 gas under low (10–400 ppm) detection limit and high operating temperature regime (25–600 °C) were studied in detail. The chemiresistive sensor exhibited high selectivity, good sensing response, fast response/recovery time with excellent stability towards H2 at high temperature. The selectivity measurement of the sensing electrode was done towards different oxidizing and reducing gases and proposed sensing mechanism discussed in detail. Therefore, the investigated Pd-Pt/SiC/PSi structure may be a highly sensitive and selective hydrogen gas sensing electrode for deployment in extreme environment applications.

Keywords: RF Sputtering, silicon carbide, porous silicon, hydrogen gas sensor

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Authors: Mamta Rana, K. K. Singh, Nisha Kumari

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The simulation models with its soil-weather-plant atmosphere interacting system are important tools for assessing the crops in changing climate conditions. The CERES-Wheat & Rice vs. 4.6 DSSAT was calibrated and evaluated for one of the major producers of wheat and rice state- Haryana, India. The simulation runs were made under irrigated conditions and three fertilizer applications dose of N-P-K to estimate crop yield and other growth parameters along with the phenological development of the crop. The genetic coefficients derived by iteratively manipulating the relevant coefficients that characterize the phenological process of wheat and rice crop to the best fit match between the simulated and observed anthesis, physological maturity and final grain yield. The model validated by plotting the simulated and remote sensing derived LAI. LAI product from remote sensing provides the edge of spatial, timely and accurate assessment of crop. For validating the yield and yield components, the error percentage between the observed and simulated data was calculated. The analysis shows that the model can be used to simulate crop yield and yield components for wheat and rice cultivar under different management practices. During the validation, the error percentage was less than 10%, indicating the utility of the calibrated model for climate risk assessment in the selected region.

Keywords: simulation model, CERES-wheat and rice model, crop yield, genetic coefficient

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Authors: Pukhtoon Yar

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Peshawar City is experiencing a rapid spatial urban growth primarily as a result of high rate of urbanization along with economic development. This paper was designed to understand the impacts of urbanization on agriculture land use change by particularly focusing on land use change trajectories from the past (1991-2014). We used Landsat imageries (30 meters) for1991along with Spot images (2.5 meters) for year 2014. . The ground truthing of the satellite data was performed by collecting information from Peshawar Development Authority, revenue department, real estate agents and interviews with the officials of city administration. The temporal satellite images were processed by applying supervised maximum likelihood classification technique in ArcGIS 9.3. The procedure resulted into five main classes of land use i.e. built-up area, farmland, barren land, cultivable-wasteland and water bodies. The analysis revealed that, in Peshawar City the built-up environment has been doubled from 8.1 percent in 1991 to over 18.2 percent in 2014 by predominantly encroaching land producing food. Furthermore, the CA-Markov Model predicted that the area under impervious surfaces would continue to flourish during the next three decades. This rapid increase in built-up area is accredited to the lack of proper land use planning and management, which has caused chaotic urban sprawl with detrimental social and environmental consequences.

Keywords: Urban Expansion, Land use, GIS, Remote Sensing, Markov Model, Peshawar City

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Authors: A. Saghatelyan, Sh. Asmaryan, G. Tepanosyan, V. Muradyan

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One of major concern to Armenia has been soil degradation emerged as a result of unsustainable management and use of grasslands, this in turn largely impacting environment, agriculture and finally human health. Hence, assessment of soil degradation is an essential and urgent objective set out to measure its possible consequences and develop a potential management strategy. Since recently, an essential tool for assessing pasture degradation has been remote sensing (RS) technologies. This research was done with an intention to measure preciseness of Linear spectral unmixing (LSU) and NDVI-SMA methods to estimate soil surface components related to degradation (fractional vegetation cover-FVC, bare soils fractions, surface rock cover) and determine appropriateness of these methods for mapping man-induced soil degradation in high mountain pastures. Taking into consideration a spatially complex and heterogeneous biogeophysical structure of the studied site, we used high resolution multispectral QuickBird imagery of a pasture site in one of Armenia’s rural communities - Nerkin Sasoonashen. The accuracy assessment was done by comparing between the land cover abundance data derived through RS methods and the ground truth land cover abundance data. A significant regression was established between ground truth FVC estimate and both NDVI-LSU and LSU - produced vegetation abundance data (R2=0.636, R2=0.625, respectively). For bare soil fractions linear regression produced a general coefficient of determination R2=0.708. Because of poor spectral resolution of the QuickBird imagery LSU failed with assessment of surface rock abundance (R2=0.015). It has been well documented by this particular research, that reduction in vegetation cover runs in parallel with increase in man-induced soil degradation, whereas in the absence of man-induced soil degradation a bare soil fraction does not exceed a certain level. The outcomes show that the proposed method of man-induced soil degradation assessment through FVC, bare soil fractions and field data adequately reflects the current status of soil degradation throughout the studied pasture site and may be employed as an alternate of more complicated models for soil degradation assessment.

Keywords: Armenia, linear spectral unmixing, remote sensing, soil degradation

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Alicja Starczewska, Aleksander Nawrat, Krzysztof Daniec, Jarosław Homa, Kacper Hołda

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Border Gateway Protocol is the main routing protocol that enables routing establishment between all autonomous systems, which are the basic administrative units of the internet. Due to the poor protection of BGP, it is important to use additional BGP security systems. Many solutions to this problem have been proposed over the years, but none of them have been implemented on a global scale. This article describes a system capable of building images of real-time BGP network topology in order to detect BGP anomalies. Our proposal performs a detailed analysis of BGP messages that come into local network cards supplemented by information collected by remote collectors in different localizations.

Keywords: BGP, BGP hijacking, cybersecurity, detection

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Authors: Launcelot McGrath

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The Coronavirus disease 2019 (COVID-19) has spread rapidly around the world, resulting in high mortality rates and very large numbers of people requiring medical treatment in ICU. Management of patient hospitalisation is a critical aspect to control this disease and reduce chaos in the healthcare systems. Remote monitoring provides a solution to protect vulnerable and elderly high-risk patients. Continuous remote monitoring of oxygen saturation, respiratory rate, heart rate, and temperature, etc., provides medical systems with up-to-the-minute information about their patients' statuses. Remote monitoring also limits the spread of infection by reducing hospital overcrowding. This paper examines the potential of remote monitoring for Covid-19 to assist in the rapid identification of patients at risk, facilitate the detection of patient deterioration, and enable early interventions.

Keywords: remote monitoring, patient care, oxygen saturation, Covid-19, hospital management

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Authors: Amr S. Fahil, Eman Ghoneim

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Egypt made significant efforts during the past few years to discover significant renewable energy sources. Regions in Egypt that have been identified for geothermal potential investigation include the Gulf of Suez and the Western Desert. One of the most promising sites for the development of Egypt's Northern Western Desert is Siwa Oasis. The geological setting of the oasis, a tectonically generated depression situated in the northernmost region of the Western desert, supports the potential for substantial geothermal resources. Field data obtained from 27 deep oil wells along the Western Desert included bottom-hole temperature (BHT) depth to basement measurements, and geological maps; data were utilized in this study. The major lithological units, elevation, surface gradient, lineaments density, and remote sensing multispectral and topographic were mapped together to generate the related physiographic variables. Eleven thematic layers were integrated in a geographic information system (GIS) to create geothermal maps to aid in the detection of significant potential geothermal spots along the Siwa Oasis and its vicinity. The contribution of total magnetic intensity data with reduction to the pole (RTP) to the first investigation of the geothermal potential in Siwa Oasis is applied in this work. The integration of geospatial data with magnetic field measurements showed a clear correlation between areas of high heat flow and magnetic anomalies. Such anomalies can be interpreted as related to the existence of high geothermal energy and dense rock, which also have high magnetic susceptibility. The outcomes indicated that the study area has a geothermal gradient ranging from 18 to 42 °C/km, a heat flow ranging from 24.7 to 111.3 m.W. k−1, a thermal conductivity of 1.3–2.65 W.m−1.k−1 and a measured amplitude temperature maximum of 100.7 °C. The southeastern part of the Siwa Oasis, and some sporadic locations on the eastern section of the oasis were found to have significant geothermal potential; consequently, this location is suitable for future geothermal investigation. The adopted method might be applied to identify significant prospective geothermal energy locations in other regions of Egypt and East Africa.

Keywords: magnetic data, SRTM, depth to basement, remote sensing, GIS, geothermal gradient, heat flow, thermal conductivity

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Authors: Fathi Kallel, Saida Khachira, Mohamed Ben Slima, Ahmed Ben Hamida

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Speckle noise is a main characteristic of cardiac ultrasound images, it corresponding to grainy appearance that degrades the image quality. For this reason, the ultrasound images are difficult to use automatically in clinical use, then treatments are required for this type of images. Then a filtering procedure of these images is necessary to eliminate the speckle noise and to improve the quality of ultrasound images which will be then segmented to extract the necessary forms that exist. In this paper, we present the importance of the pre-treatment step for segmentation. This work is applied to cardiac ultrasound images. In a first step, a comparative study of speckle filtering method will be presented and then we use a segmentation algorithm to locate and extract cardiac structures.

Keywords: medical image processing, ultrasound images, Speckle noise, image enhancement, speckle filtering, segmentation, snakes

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Authors: Emhimed Saffor

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In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications.

Keywords: CT images, Matlab, medical images, edge detection

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Authors: Muhittin Karaman, Murat Budakoglu

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In this study, derivation of lake bathymetry was evaluated using the high resolution Worldview-2 multispectral images in the very shallow hypersaline Lake Acıgöl which does not have a stable water table due to the wet-dry season changes and industrial usage. Every year, a great part of the lake water budget has been consumed for the industrial salt production in the evaporation ponds, which are generally located on the south and north shores of Lake Acıgöl. Therefore, determination of the water level changes from a perspective of remote sensing-based lake water by bathymetry studies has a great importance in the sustainability-control of the lake. While the water table interval is around 1 meter between dry and wet season, dissolved ion concentration, salinity and turbidity also show clear differences during these two distinct seasonal periods. At the same time, with the satellite data acquisition (June 9, 2013), a field study was conducted to collect the salinity values, Secchi disk depths and turbidity levels. Max depth, Secchi disk depth and salinity were determined as 1,7 m, 0,9 m and 43,11 ppt, respectively. Eight-band Worldview-2 image was corrected for atmospheric effects by ATCOR technique. For each sampling point in the image, mean reflectance values in 1*1, 3*3, 5*5, 7*7, 9*9, 11*11, 13*13, 15*15, 17*17, 19*19, 21*21, 51*51 pixel reflectance neighborhoods were calculated separately. A unique image has been derivated for each matrix resolution. Spectral values and depth relation were evaluated for these distinct resolution images. Correlation coefficients were determined for the 1x1 matrix: 0,98, 0,96, 0,95 and 0,90 for the 724 nm, 831 nm, 908 nm and 659 nm, respectively. While 15x5 matrix characteristics with 0,98, 0,97 and 0,97 correlation values for the 724 nm, 908 nm and 831 nm, respectively; 51x51 matrix shows 0,98, 0,97 and 0,96 correlation values for the 724 nm, 831 nm and 659 nm, respectively. Comparison of all matrix resolutions indicates that RedEdge band (724 nm) of the Worldview-2 satellite image has the best correlation with the saline shallow lake of Acıgöl in-situ depth.

Keywords: bathymetry, Worldview-2 satellite image, ATCOR technique, Lake Acıgöl, Denizli, Turkey

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Authors: Reiner-Ace P. Mateo, Vince Paolo F. Obille

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The traditional method of classifying the potential mineral resources requires a significant amount of time and money. In this paper, an alternative way to classify potential mineral resources with GIS application in Surigao del Sur. The three (3) analog map data inputs integrated to GIS are geologic map, topographic map, and land cover/vegetation map. The indicators used in the classification of potential nickel laterite integrated from the analog map data inputs are a geologic indicator, which is the presence of ultramafic rock from the geologic map; slope indicator and the presence of plateau edges from the topographic map; areas of forest land, grassland, and shrublands from the land cover/vegetation map. The potential mineral of the area was classified from low up to very high potential. The produced mineral potential classification map of Surigao del Sur has an estimated 4.63% low nickel laterite potential, 42.15% medium nickel laterite potential, 43.34% high nickel laterite potential, and 9.88% very high nickel laterite from its ultramafic terrains. For the validation of the produced map, it was compared with known occurrences of nickel laterite in the area using a nickel mining tenement map from the area with the application of remote sensing. Three (3) prominent nickel mining companies were delineated in the study area. The generated potential classification map of nickel-laterite in Surigao Del Sur may be of aid to the mining companies which are currently in the exploration phase in the study area. Also, the currently operating nickel mines in the study area can help to validate the reliability of the mineral classification map produced.

Keywords: mineral potential classification, nickel laterites, GIS, remote sensing, Surigao del Sur

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Authors: Nur Afiqah Mohd Kamal, Khamarrul Azahari Razak

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The earthquake aftermath has become a major concern, especially in high seismicity region. In Kundasang, Sabah, the earthquake on 5th June 2015 resulted in several catastrophes; landslides, rockfalls, mudflows and major slopes affected regardless of the series of the aftershocks. Certainly, the consequences of earthquake generate and induce the episodic disaster, not only life-threatening but it also affects infrastructure and economic development. Therefore, a need for investigating the change in land use and land cover (LULC) of post-earthquake geohazard is essential for identifying the extent of disastrous effects towards the development in Kundasang. With the advancement of remote sensing technology, post-earthquake geohazards (landslides, mudflows, rockfalls, debris flows) assessment can be evaluated by the employment of object-based image analysis in investigating the LULC change which consists of settlements, public infrastructure and vegetation cover. Therefore, this paper discusses the preliminary results on post-earthquakes geohazards distribution in Kundasang and evaluates the LULC classification effect upon the occurrences of geohazards event. The result of this preliminary analysis will provide an overview to determine the extent of geohazard impact on LULC. This research also provides beneficial input to the local authority in Kundasang about the risk of future structural development on the geohazard area.

Keywords: geohazard, land use land cover, object-based image analysis, remote sensing

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Authors: Lori Timmis

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The COVID-19 pandemic precipitated significant organizational change in employee turnover, retirements, and burnout exacerbated by enrollment declines in higher education, especially community colleges. To counter this downturn, community college leaders must thoughtfully examine meaningful work opportunities to retain an engaged and productive workforce. Higher education led fully remote teams during the pandemic, which highlighted the benefits and weaknesses of building and leading remote teams. Hybrid remote teams offer possibility to reimagine community college structures, though leading remote teams requires specific e-leadership competencies. This paper examines the literature of studies on e-leadership conducted during the pandemic and from several higher education studies, pre-pandemic, against an e-leadership competency framework. The e-leadership studies conducted pre-pandemic and from the pandemic complement the e-leadership competency framework, comprising six e-leadership competencies performed via information technology communications, which provides community college (and higher education) leaders to consider hybrid remote team structures and the necessary leadership skills to lead hybrid remote teams.

Keywords: community college, e-leadership, great resignation, hybrid remote teams

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Authors: Neha Gupta

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Landslide vulnerability is considered as the crucial parameter for the assessment of landslide risk. The term vulnerability defined as the damage or degree of elements at risk of different dimensions, i.e., physical, social, economic, and environmental dimensions. Himalaya region is very prone to multi-hazard such as floods, forest fires, earthquakes, and landslides. With the increases in fatalities rates, loss of infrastructure, and economy due to landslide in the Himalaya region, leads to the assessment of vulnerability. In this study, a methodology to measure the combination of vulnerability dimension, i.e., social vulnerability, physical vulnerability, and environmental vulnerability in one framework. A combined result of these vulnerabilities has rarely been carried out. But no such approach was applied in the Indian Scenario. The methodology was applied in an area of east Sikkim Himalaya, India. The physical vulnerability comprises of building footprint layer extracted from remote sensing data and Google Earth imaginary. The social vulnerability was assessed by using population density based on land use. The land use map was derived from a high-resolution satellite image, and for environment vulnerability assessment NDVI, forest, agriculture land, distance from the river were assessed from remote sensing and DEM. The classes of social vulnerability, physical vulnerability, and environment vulnerability were normalized at the scale of 0 (no loss) to 1 (loss) to get the homogenous dataset. Then the Multi-Criteria Analysis (MCA) was used to assign individual weights to each dimension and then integrate it into one frame. The final vulnerability was further classified into four classes from very low to very high.

Keywords: landslide, multi-criteria analysis, MCA, physical vulnerability, social vulnerability

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: Deepak Khare, Radha Krishan, Bhaskar Nikam

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The present study deals with the results of the Ortho-rectified Cartosat-1 PAN (2.5 m resolution) satellite data analysis for the extraction of canal networks under the Eastern Ganga Canal (EGC) command. Based on the information derived through the remote sensing data, in terms of the number of canals, their physical status and hydraulic connectivity from the source, irrigation potential (IP) created in the command was assessed by comparing with planned/design canal-wise irrigation potentials. All the geospatial information generated in the study is organized in a geodatabase. The EGC project irrigates the command through one main canal, five branch canals, 36 distributaries and 186 minors. The study was conducted with the main objectives of inventory and mapping of irrigation infrastructure using geographic information system (GIS), remote sensing and field data. Likewise, the assessment of irrigation potential created using the mapped infrastructure was performed as on March 2017. Results revealed that the canals were not only pending but were also having gap/s, and hydraulically disconnected in each branch canal and also in minors of EGC. A total of 16622.3 ha of commands were left untouched with canal water just due to the presence of gaps in the EGC project. The sum of all the gaps present in minor canals was 11.92 km, while in distributary, it was 2.63 km. This is a very good scenario that balances IP can be achieved by working on the gaps present in minor canals. Filling the gaps in minor canals can bring most of the area under irrigation, especially the tail reaches command.

Keywords: canal command, GIS, hydraulic connectivity, irrigation potential

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Abdallah Ghoul, Bachir Ouamri, Ismail Khalil Bousserhane

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In this paper, we have developed a sliding mode controller for PUMA 600 manipulator robot, to control the remote robot a teleoperation system was developed. This system includes two sites, local and remote. The sliding mode controller is installed at the remote site. The client asks for a position through an interface and receives the real positions after running of the task by the remote robot. Both sites are interconnected via the Internet. In order to verify the effectiveness of the sliding mode controller, that is compared with a classic PID controller. The developed approach is tested on a virtual robot. The results confirmed the high performance of this approach.

Keywords: internet, manipulator robot, PID controller, remote control, sliding mode, teleoperation

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Authors: Shuen-Tai Wang, Hsi-Ya Chang

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Cloud virtualization technologies are becoming more and more prevalent, cloud users usually encounter the problem of how to access to the virtualized remote desktops easily over the web without requiring the installation of special clients. To resolve this issue, we took advantage of the HTML5 technology and developed web-based remote desktop. It permits users to access the terminal which running in our cloud platform from anywhere. We implemented a sketch of web interface following the cloud computing concept that seeks to enable collaboration and communication among users for high performance computing. Given the development of remote desktop virtualization, it allows to shift the user’s desktop from the traditional PC environment to the cloud platform, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. This is also made possible by the low administrative costs as well as relatively inexpensive end-user terminals and reduced energy expenses.

Keywords: virtualization, remote desktop, HTML5, cloud computing

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Authors: Dali Nikolaishvili, Nino Khareba, Mariam Tsitsagi

Abstract:

Tbilisi has been a capital of Georgia since the 5ᵗʰ century. City area was covered by forest in historical past. Nowadays the situation has been changing dramatically. Dozens of problems are caused by damages/destruction of green cover and solution, at one glance, seems to be uncomplicated (planting trees and creating green quarters), but on the other hand, according to the increasing tendency, the built up of areas still remains unsolved. Finding out the ways to overcome such obstacles is important even for protecting the health of society. Making of Retrospective cartography of the forest area of Tbilisi with use of GIS technology and remote sensing was the main aim of the research. Research about the dynamic of forest-cover in Tbilisi and its surroundings included the following steps: assessment of the dynamic of forest in Tbilisi and its surroundings. The survey was mainly based on the retrospective mapping method. Using of GIS technology, studying, comparing and identifying the narrative sources was the next step. And the last one was analyzed of the changes from the 80s to the present days on the basis of decryption of remotely sensed images. After creating a unified cartographic basis, the mapping and plans of different periods have been linked to this geodatabase. Data about green parks, individual old plants existing in the private yards and respondents' Information (according to a questionnaire created in advance) was added to the basic database, the general plan of Tbilisi and Scientific works as well. On the basis of analysis of historic, including cartographic sources, forest-cover maps for different periods of time were made. In addition, was made the catalog of individual green parks (location, area, typical composition, name and so on), which was the basis of creating several thematic maps. Areas with a high rate of green area degradation were identified. Several maps depicting the dynamics of forest cover of Tbilisi were created and analyzed. The methods of linking the data of the old cartographic sources to the modern basis were developed too, the result of which may be used in Urban Planning of Tbilisi. Understanding, perceiving and analyzing the real condition of green cover in Tbilisi and its problems, in turn, will help to take appropriate measures for the maintenance of ancient plants, to develop forests and to plan properly parks, squares, and recreational sites. Because the healthy environment is the main condition of human health and implies to the rational development of the city.

Keywords: catalogue of green area, GIS, historical cartography, cartography, remote sensing, Tbilisi

Procedia PDF Downloads 113

Authors: Khalid Hussein, Waleed Abdalati, Pakorn Petchprayoon, Khaula Alkaabi

Abstract:

In this study, we integrated ASTER thermal data with an area-based spatial insolation model to identify and delineate geothermally active areas in Yellowstone National Park (YNP). Two pairs of L1B ASTER day- and nighttime scenes were used to calculate land surface temperature. We employed the Emissivity Normalization Algorithm which separates temperature from emissivity to calculate surface temperature. We calculated the incoming solar radiation for the area covered by each of the four ASTER scenes using an insolation model and used this information to compute temperature due to solar radiation. We then identified the statistical thermal anomalies using land surface temperature and the residuals calculated from modeled temperatures and ASTER-derived surface temperatures. Areas that had temperatures or temperature residuals greater than 2σ and between 1σ and 2σ were considered ASTER-modeled thermal anomalies. The areas identified as thermal anomalies were in strong agreement with the thermal areas obtained from the YNP GIS database. Also the YNP hot springs and geysers were located within areas identified as anomalous thermal areas. The consistency between our results and known geothermally active areas indicate that thermal remote sensing data, integrated with a spatial-based insolation model, provides an effective means for identifying and locating areas of geothermal activities over large areas and rough terrain.

Keywords: thermal remote sensing, insolation model, land surface temperature, geothermal anomalies

Procedia PDF Downloads 339

Authors: Mejía M. Paula, Ramírez L. Leonardo, Puerta A. Gabriel

Abstract:

In telemedicine, the image repository service is important to increase the accuracy of diagnostic support of medical personnel. This study makes comparison between two routing algorithms regarding the quality of service (QoS), to be able to analyze the optimal performance at the time of loading and/or downloading of medical images. This study focused on comparing the performance of Tabu Search with other heuristic and metaheuristic algorithms that improve QoS in telemedicine services in Colombia. For this, Tabu Search and Simulated Annealing heuristic algorithms are chosen for their high usability in this type of applications; the QoS is measured taking into account the following metrics: Delay, Throughput, Jitter and Latency. In addition, routing tests were carried out on ten images in digital image and communication in medicine (DICOM) format of 40 MB. These tests were carried out for ten minutes with different traffic conditions, reaching a total of 25 tests, from a server of Universidad Militar Nueva Granada (UMNG) in Bogotá-Colombia to a remote user in Universidad de Santiago de Chile (USACH) - Chile. The results show that Tabu search presents a better QoS performance compared to Simulated Annealing, managing to optimize the routing of medical images, a basic requirement to offer diagnostic images services in telemedicine.

Keywords: medical image, QoS, simulated annealing, Tabu search, telemedicine

Procedia PDF Downloads 192