Search results for: spectral bands

Authors: Golayeh Yousefi, Mehdi Homaee, Ali Akbar Norouzi

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Ongoing monitoring of soil contamination by heavy metals is critical for ecosystem stability and environmental protection, and food security. The conventional methods of determining these soil contaminants are time-consuming and costly. Spectroscopy in the visible near-infrared (VNIR) - short wave infrared (SWIR) region is a rapid, non-destructive, noninvasive, and cost-effective method for assessment of soil heavy metals concentration by studying the spectral properties of soil constituents. The aim of this study is to derive spectral bands and important ranges that are sensitive to heavy metals and can be used to estimate the concentration of these soil contaminants. In other words, the change in the spectral properties of spectrally active constituents of soil can lead to the accurate identification and estimation of the concentration of these compounds in soil. For this purpose, 325 soil samples were collected, and their spectral reflectance curves were evaluated at a range of 350-2500 nm. After spectral preprocessing operations, the partial least-squares regression (PLSR) model was fitted on spectral data to predict the concentration of Cu and Ni. Based on the results, the spectral range of Cu- sensitive spectra were 480, 580-610, 1370, 1425, 1850, 1920, 2145, and 2200 nm, and Ni-sensitive ranges were 543, 655, 761, 1003, 1271, 1415, 1903, 2199 nm. Finally, the results of this study indicated that the spectral data contains a lot of information that can be applied to identify the soil properties, such as the concentration of heavy metals, with more detail.

Keywords: heavy metals, spectroscopy, spectral bands, PLS regression

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Authors: Akrem Sellami, Imed Riadh Farah, Basel Solaiman

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With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.

Keywords: band selection, dimensionality reduction, feature extraction, hyperspectral imagery, semantic interpretation

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Mohamed A. Almoghalis, Osman M. Hegazy, Ibrahim F. Imam, Ali H. Elbastawessy

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This paper presents a new hyperspectral image segmentation scheme with respect to both spatial and spectral contexts. The scheme uses the 8-pixels spatial pattern to build a weight structure that holds the number of outlier bands for each pixel among its neighborhood windows in different directions. The number of outlier bands for a pixel is obtained using bands quartile box plots profile among spatial 8-pixels pattern windows. The quartile box plot weight structure represents the spatial-spectral context in the image. Instead of starting segmentation process by single pixels, the proposed methodology starts by pixels groups that proved to share the same spectral features with respect to their spatial context. As a result, the segmentation scheme starts with Jigsaw pieces that build a mosaic image. The following step builds a model for each Jigsaw piece in the mosaic image. Each Jigsaw piece will be merged with another Jigsaw piece using KNN applied to their bands' quartile box plots profiles. The scheme iterates till required number of segments reached. Experiments use two data sets obtained from Earth Observer 1 (EO-1) sensor for Egypt and France. Initial results qualitative analysis showed encouraging results compared with ground truth. Quantitative analysis for the results will be included in the final paper.

Keywords: hyperspectral image segmentation, image processing, remote sensing, box plot

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

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Authors: Tuba Zahra, Aakash Parekh

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Hyperspectral remote sensing refers to imaging of objects or materials in narrow conspicuous spectral bands. Hyperspectral images (HSI) enable the extraction of spectral signatures for objects or materials observed. These images contain information about the reflectance of each pixel across the electromagnetic spectrum. It enables the acquisition of data simultaneously in hundreds of spectral bands with narrow bandwidths and can provide detailed contiguous spectral curves that traditional multispectral sensors cannot offer. The contiguous, narrow bandwidth of hyperspectral data facilitates the detailed surveying of Earth's surface features. This would otherwise not be possible with the relatively coarse bandwidths acquired by other types of imaging sensors. Hyperspectral imaging provides significantly higher spectral and spatial resolution. There are several use cases that represent the commercial applications of hyperspectral remote sensing. Each use case represents just one of the ways that hyperspectral satellite imagery can support operational efficiency in the respective vertical. There are some use cases that are specific to VNIR bands, while others are specific to SWIR bands. This paper discusses the different commercially viable use cases that are significant for HSI application areas, such as agriculture, mining, oil and gas, defense, environment, and climate, to name a few. Theoretically, there is n number of use cases for each of the application areas, but an attempt has been made to streamline the use cases depending upon economic feasibility and commercial viability and present a review of literature from this perspective. Some of the specific use cases with respect to agriculture are crop species (sub variety) detection, soil health mapping, pre-symptomatic crop disease detection, invasive species detection, crop condition optimization, yield estimation, and supply chain monitoring at scale. Similarly, each of the industry verticals has a specific commercially viable use case that is discussed in the paper in detail.

Keywords: agriculture, mining, oil and gas, defense, environment and climate, hyperspectral, VNIR, SWIR

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Authors: Abdelhamid Zerroug, Mlle Ismahene Sehili

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Spectral methods are usually applied to solve uni-dimensional boundary value problems. With the advantage of the creation of multidimensional basis, we propose a new spectral method for bi-dimensional problems. In this article, we start by creating bi-spectral basis by different ways, we developed also a new relations to determine the expressions of spectral coefficients in different partial derivatives expansions. Finally, we propose the principle of a new bi-spectral method for the bi-dimensional problems.

Keywords: boundary value problems, bi-spectral methods, bi-dimensional Legendre basis, spectral method

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Authors: Zahraa Al Ali, Ammar Abulibdeh, Talal Al-Awadhi, Midhun Mohan, Mohammed Al-Barwani, Mohammed Al-Barwani, Sara Al Nabbi, Meshal Abdullah

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This study aims to evaluate the performance and effectiveness of six spectral water indices - derived from Multispectral sentinel-2 data - to detect soil moisture and inundated area in arid regions to be used as an indicator of mudflow phenomena to predict high-risk areas. Herein, the validation of the performance of spectral indices was conducted using threshold method, spectral curve performance, and soil-line method. These indirect validation techniques play a key role in saving time, effort, and cost, particularly for large-scale and inaccessible areas. It was observed that the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (mNDWI), and RSWIR indices have the potential to detect soil moisture and inundated areas in arid regions. According to the temporal spectral curve performance, the spectral characteristics of water and soil moisture were distinct in the Near infrared (NIR), Short-wave Infrared (SWIR1,2) bands. However, the rate and degree differed between these bands, depending on the amount of water in the soil. Furthermore, the soil line method supported the appropriate selection of threshold values to detect soil moisture. However, the threshold values varied with location, time, season, and between indices. We concluded that considering the factors influencing the behavior of water and soil reflectivity could support decision-makers in identifying high-risk mudflow locations.

Keywords: spectral reflectance curve, soil-line method, spectral indices, Shaheen cyclone

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Authors: Yonwaba Atyosi, Moses Cho, Abel Ramoelo, Nobuhle Majozi, Cecilia Masemola, Yoliswa Mkhize

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Spectroscopy has been widely used to understand the hyperspectral remote sensing of soils. Accurate and efficient measurement of soil moisture is essential for precision agriculture. The aim of this study was to understand the spectral behavior of soil at different soil water content levels and identify the significant spectral bands for soil moisture content retrieval at field-scale. The study consisted of 60 soil samples from a maize farm, divided into four different treatments representing different moisture levels. Spectral signatures were measured for each sample in laboratory under artificial light using an Analytical Spectral Device (ASD) spectrometer, covering a wavelength range from 350 nm to 2500 nm, with a spectral resolution of 1 nm. The results showed that the absorption features at 1450 nm, 1900 nm, and 2200 nm were particularly sensitive to soil moisture content and exhibited strong correlations with the water content levels. Continuum removal was developed in the R programming language to enhance the absorption features of soil moisture and to precisely understand its spectral behavior at different water content levels. Statistical analysis using partial least squares regression (PLSR) models were performed to quantify the correlation between the spectral bands and soil moisture content. This study provides insights into the spectral behavior of soil at different water content levels and identifies the significant spectral bands for soil moisture content retrieval. The findings highlight the potential of spectroscopy for non-destructive and rapid soil moisture measurement, which can be applied to various fields such as precision agriculture, hydrology, and environmental monitoring. However, it is important to note that the spectral behavior of soil can be influenced by various factors such as soil type, texture, and organic matter content, and caution should be taken when applying the results to other soil systems. The results of this study showed a good agreement between measured and predicted values of Soil Moisture Content with high R2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil samples. The results has significant implications for developing high-resolution and precise field-scale soil moisture retrieval models. These models can be used to understand the spatial and temporal variation of soil moisture content in agricultural fields, which is essential for managing irrigation and optimizing crop yield.

Keywords: soil moisture content retrieval, precision agriculture, continuum removal, remote sensing, machine learning, spectroscopy

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Authors: Fathi Kallel, Ahmed Ben Hamida, Christian Berger-Vachon

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In this paper, a Speech Enhancement Algorithm based on Multi-Band Spectral Subtraction (MBSS) principle is evaluated for Bilateral Cochlear Implant (BCI) users. Specifically, dual-channel noise power spectral estimation algorithm using Power Spectral Densities (PSD) and Cross Power Spectral Densities (CPSD) of the observed signals is studied. The enhanced speech signal is obtained using Dual-Channel Multi-Band Spectral Subtraction ‘DC-MBSS’ algorithm. For performance evaluation, objective speech assessment test relying on Perceptual Evaluation of Speech Quality (PESQ) score is performed to fix the optimal number of frequency bands needed in DC-MBSS algorithm. In order to evaluate the speech intelligibility, subjective listening tests are assessed with 3 deafened BCI patients. Experimental results obtained using French Lafon database corrupted by an additive babble noise at different Signal-to-Noise Ratios (SNR) showed that DC-MBSS algorithm improves speech understanding for single and multiple interfering noise sources.

Keywords: speech enhancement, spectral substracion, noise estimation, cochlear impalnt

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Authors: Ramandeep Kaur M. Malhi, Prashant K. Srivastava, G.Sandhya Kiran

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Quantitative estimates of foliar biochemicals, namely chlorophyll content (CC), serve as key information for the assessment of plant productivity, stress, and the availability of nutrients. This also plays a critical role in predicting the dynamic response of any vegetation to altering climate conditions. The advent of hyperspectral data with an enhanced number of available wavelengths has increased the possibility of acquiring improved information on CC. Retrieval of CC is extensively carried through well known spectral indices derived from hyperspectral data. In the present study, an attempt is made to develop hyperspectral indices by identifying optimum bands for CC estimation in Butea monosperma (Lam.) Taub growing in forests of Shoolpaneshwar Wildlife Sanctuary, Narmada district, Gujarat State, India. 196 narrow bands of EO-1 Hyperion images were screened, and the best optimum wavelength from blue, green, red, and near infrared (NIR) regions were identified based on the coefficient of determination (R²) between band reflectance and laboratory estimated CC. The identified optimum wavelengths were then employed for developing 12 hyperspectral indices. These spectral index values and CC values were then correlated to investigate the relation between laboratory measured CC and spectral indices. Band 15 of blue range and Band 22 of green range, Band 40 of the red region, and Band 79 of NIR region were found to be optimum bands for estimating CC. The optimum band based combinations on hyperspectral data proved to be the most effective indices for quantifying Butea CC with NDVI and TVI identified as the best (R² > 0.7, p < 0.01). The study demonstrated the significance of band characterization in the development of the best hyperspectral indices for the chlorophyll estimation, which can aid in monitoring the vitality of forests.

Keywords: band, characterization, chlorophyll, hyperspectral, indices

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Malik Abid Hussain Khokhar, Muhammad Adnan Tahir, Hisham Bin Hafeez Awan

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Continuous environmental determinism and climatic change in the entire globe due to increasing land surface temperature (LST) has become a vital phenomenon nowadays. LST is accelerating because of increasing greenhouse gases in the environment which results of melting down ice caps, ice sheets and glaciers. It has not only worse effects on vegetation and water bodies of the region but has also severe impacts on monsoon areas in the form of capricious rainfall and monsoon failure extensive precipitation. Environment can be monitored with the help of various geographic information systems (GIS) based algorithms i.e. SC (Single), DA (Dual Angle), Mao, Sobrino and SW (Split Window). Estimation of LST is very much possible from digital image processing of satellite imagery. This paper will encompass extraction of LST of Abbottabad using SW technique of GIS and Remote Sensing over last ten years by means of Landsat 7 ETM+ (Environmental Thematic Mapper) and Landsat 8 vide their Thermal Infrared (TIR Sensor) and Optical Land Imager (OLI sensor less Landsat 7 ETM+) having 100 m TIR resolution and 30 m Spectral Resolutions. These sensors have two TIR bands each; their emissivity and spectral radiance will be used as input statistics in SW algorithm for LST extraction. Emissivity will be derived from Normalized Difference Vegetation Index (NDVI) threshold methods using 2-5 bands of OLI with the help of e-cognition software, and spectral radiance will be extracted TIR Bands (Band 10-11 and Band 6 of Landsat 7 ETM+). Accuracy of results will be evaluated by weather data as well. The successive research will have a significant role for all tires of governing bodies related to climate change departments.

Keywords: environment, Landsat 8, SW Algorithm, TIR

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Authors: Karima Kimouche

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In this paper, we consider the asymptotic problems in spectral analysis of stationary causal random fields. We impose conditions only involving (conditional) moments, which are easily verifiable for a variety of nonlinear random fields. Limiting distributions of periodograms and smoothed periodogram spectral density estimates are obtained and applications to the spectral domain bootstrap are given.

Keywords: spatial nonlinear processes, spectral estimators, GMC condition, bootstrap method

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Authors: Mouhamadou Dosso

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This paper presents methods of spectral dichotomy of a matrix which compute spectral projectors on the subspace associated with the eigenvalues external to the parabolas described by a general equation. These methods are modifications of the one proposed in [A. N. Malyshev and M. Sadkane, SIAM J. MATRIX ANAL. APPL. 18 (2), 265-278, 1997] which uses the spectral dichotomy method of a matrix with respect to the imaginary axis. Theoretical and algorithmic aspects of the methods are developed. Numerical results obtained by applying methods presented on matrices are reported.

Keywords: spectral dichotomy method, spectral projector, eigensubspaces, eigenvalue

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Authors: Geetika Barman, B. S. Daya Sagar

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In this article, we proposed a pixel-wise classification of hyperspectral images using a mathematical morphology operator-based distance metric called “dilation distance” and “erosion distance”. This method involves measuring the spatial distance between the spectral features of a hyperspectral image across the bands. The key concept of the proposed approach is that the “dilation distance” is the maximum distance a pixel can be moved without changing its classification, whereas the “erosion distance” is the maximum distance that a pixel can be moved before changing its classification. The spectral signature of the hyperspectral image carries unique class information and shape for each class. This article demonstrates how easily the dilation and erosion distance can measure spatial distance compared to other approaches. This property is used to calculate the spatial distance between hyperspectral image feature vectors across the bands. The dissimilarity matrix is then constructed using both measures extracted from the feature spaces. The measured distance metric is used to distinguish between the spectral features of various classes and precisely distinguish between each class. This is illustrated using both toy data and real datasets. Furthermore, we investigated the role of flat vs. non-flat structuring elements in capturing the spatial features of each class in the hyperspectral image. In order to validate, we compared the proposed approach to other existing methods and demonstrated empirically that mathematical operator-based distance metric classification provided competitive results and outperformed some of them.

Keywords: dilation distance, erosion distance, hyperspectral image classification, mathematical morphology

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Authors: R. Bouhennache, T. Bouden, A. A. Taleb, A. Chaddad

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In this paper we propose a method to map the urban areas. The method uses an arithmetic calculation processed from the land features indexes and Tasseled cap transformation TC of multi spectral Thematic Mapper Landsat TM image. For this purpose the derived indexes image from the original image such SAVI the soil adjusted vegetation index, UI the urban Index, and EBBI the enhanced built up and bareness index were staked to form a new image and the bands were uncorrelated, also the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification approaches were first applied on the new image TM data using the reference spectra of the spectral library and subsequently the four urban, vegetation, water and soil land cover categories were extracted with their accuracy assessment.The urban features were represented using a logic calculation applied to the brightness, UI-SAVI, NDBI-greenness and EBBI- brightness data sets. The study applied to Blida and mentioned that the urban features can be mapped with an accuracy ranging from 92 % to 95%.

Keywords: EBBI, SAVI, Tasseled Cap Transformation, UI

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Authors: Waleed M. Al-Othman, Hamid Bayati, Abdullah Al-Shahrani, Haitham Al-Jabr

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Pearlite bands commonly form parallel to the surface of the hot rolled steel and have significant influence on the properties of the steel. This study investigated the correlation between segregation pattern of Mn, Si, C and formation of the pearlite bands in hot rolled Gr 60 steel plate. Microstructural study indicated formation of a distinguished thick band at centerline of the plate with number of parallel bands through thickness of the steel plate. The thickness, frequency, and continuity of the bands are reduced from mid-thickness toward external surface of the steel plate. Analysis showed a noticeable increase of C, Si and Mn levels within the bands. Such alloying segregation takes place during metal solidification. EDS analysis verified presence of particles rich in Ti, Nb, Mn, C, N, within the bands. Texture analysis by Electron Backscatter Detector (EBSD) indicated the grains size/misorientation can noticeably change within the bands. Effect of banding on through-thickness properties of the steel was examined by carrying out microhardness, toughness and tensile tests. Results suggest the Mn and C contents are changed in sinusoidal pattern through thickness of the hot rolled plate and pearlite bands are formed at the peaks of this sinusoidal segregation pattern. Changes in grain size/misorientation, formation of highly alloyed particles, and pearlite within these bands, facilitate crack formation along boundaries of these bands.

Keywords: pearlite band, alloying segregation, hot rolling, Ti, Nb, N, C

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Authors: Yessica Nataliani, Miin-Shen Yang

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Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices.

Keywords: group technology, cell formation, spectral clustering, grouping efficiency

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Authors: Muna F. Al-Sammaraie

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The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform.

Keywords: image enhancement, multi-spectral, RGB, histogram

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Neha Devi, P. K. Joshi

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Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. One of the most significant forms of global disturbance, impacting community dynamics, biogeochemical cycles and local and regional climate across a wide range of ecosystems ranging from boreal forests to tropical rainforest is wildfire Assessment of fire danger is a function of forest type, fuelwood stock volume, moisture content, degree of senescence and fire management strategy adopted in the ground. Remote sensing has potential of reduction the uncertainty in mapping fuels. Hyperspectral remote sensing is emerging to be a very promising technology for wildfire fuels characterization. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of forest fire fuels including above ground live biomass, canopy moisture, etc. We used Hyperion imagery acquired in February, 2016 and analysed four fuel characteristics using Hyperion sensor data on-board EO-1 satellite, acquired over the Shiwalik Himalayas covering the area of Champawat, Uttarakhand state. The main objective of this study was to present an overview of methodologies for mapping fuel properties using hyperspectral remote sensing data. Fuel characteristics analysed include fuel biomass, fuel moisture, and fuel condition and fuel type. Fuel moisture and fuel biomass were assessed through the expression of the liquid water bands. Fuel condition and type was assessed using green vegetation, non-photosynthetic vegetation and soil as Endmember for spectral mixture analysis. Linear Spectral Unmixing, a partial spectral unmixing algorithm, was used to identify the spectral abundance of green vegetation, non-photosynthetic vegetation and soil.

Keywords: forest fire fuel, Hyperion, hyperspectral, linear spectral unmixing, spectral mixture analysis

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Authors: Abdullah M. Alzahrani

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The unicellular alga Chlorella vulgaris was isolated from Al-Asfar Lake, which is located in the Al-Ahsa province of Saudi Arabia. Two different isolates were sub-cultured under laboratory conditions. The wild type was grown under a regular concentration of copper, whereas the other isolate was grown under a progressively increasing copper concentration. An Inter Simple Sequence Repeats (ISSR) analysis was performed using DNA isolated from the wild type and tolerant strains. The sum of the scored bands of the wild type was 155, with 100 (64.5%) considered to be polymorphic bands, whereas the resistant strain displayed 147 bands, with 92 (62.6%) considered to be polymorphic bands. The sum of the scored bands of a mixed sample was 117 bands, of which only 4 (3.4%) were considered to be polymorphic. The average Nei's genetic diversity (h) and Shannon-Weiner diversity indices (I) were 0.3891 and 0.5394, respectively. These results clearly indicate that the adaptation to a high level of copper in Chlorella vulgaris is not merely physiological but rather driven by modifications at the genomic level.

Keywords: chlorella vulgaris, copper tolerance, genetic diversity, green algae

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Authors: Chor Nejmeddine, Ines Ben Omrane, Mohamed Abdelwahed

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In this paper, we present a posteriori analysis of the discretization of the heat equation by spectral element method. We apply Euler's implicit scheme in time and spectral method in space. We propose two families of error indicators, both of which are built from the residual of the equation and we prove that they satisfy some optimal estimates. We present some numerical results which are coherent with the theoretical ones.

Keywords: heat equation, spectral elements discretization, error indicators, Euler

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Authors: Serifenur Cebesoy, Yelda Aygar, Elgiz Bairamov

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In this study, we examine some spectral properties of non-selfadjoint matrix-valued difference equations consisting of a polynomial type Jost solution. The aim of this study is to investigate the eigenvalues and spectral singularities of the difference operator L which is expressed by the above-mentioned difference equation. Firstly, thanks to the representation of polynomial type Jost solution of this equation, we obtain asymptotics and some analytical properties. Then, using the uniqueness theorems of analytic functions, we guarantee that the operator L has a finite number of eigenvalues and spectral singularities.

Keywords: asymptotics, continuous spectrum, difference equations, eigenvalues, jost functions, spectral singularities

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically.

Keywords: spectral index, shadow detection, remote sensing images, World-View 2

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Authors: Abdul Basit, Amil Daraz, Guoqiang Zhang

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With the increasing demand for wireless communication systems for unlicensed indoor applications, the FCC, in February 2002, allocated unlicensed bands ranging from 3.1 GHZ to 10.6 GHz with fractional bandwidth of about 109 %, because it plays a key role in the radiofrequency (RF) front ends devices and has been widely applied in many other microwave circuits. Targeting the proposed band defined by the FCC for the UWB system, this article presents a UWB bandpass filter with three stop bands for the mitigation of wireless bands that may interfere with the UWB range. For this purpose, two resonators are utilized for the implementation of triple-notched bands. The C-shaped resonator is used for the first notch band creation at 3.4 GHz to suppress the WiMAX signal, while the H-shaped resonator is employed in the initial UWB design to introduce the dual notched characteristic at 4.5 GHz and 8.1 GHz to reject the WLAN and Satellite Communication signals. The overall circuit area covered by the proposed design is 30.6 mm × 20 mm, or in terms of guided wavelength at the first stopband, its size is 0.06 λg × 0.02 λg. The presented structure shows a good return loss under -10 dB over most of the passband and greater than -15 dB for the notched frequency bands. Finally, the filter is simulated and analyzed in HFSS 15.0. All the bands for the rejection of wireless signals are independently controlled, which makes this work superior to the rest of the UWB filters presented in the literature.

Keywords: a bandpass filter (BPF), ultra-wideband (UWB), wireless communication, C-shaped resonator, triple notch

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Authors: Chao Xu

Abstract:

Selecting ground motion intensity measures reasonably is one of the very important issues to affect the input ground motions selecting and the reliability of vulnerability analysis results. In this paper, inelastic spectral displacement is used as an alternative intensity measure to characterize the ground motion damage potential. The inelastic spectral displacement is calculated based modal pushover analysis and inelastic spectral displacement based incremental dynamic analysis is developed. Probability seismic demand analysis of a six story and an eleven story RC frame are carried out through cloud analysis and advanced incremental dynamic analysis. The sufficiency and efficiency of inelastic spectral displacement are investigated by means of regression and residual analysis, and compared with elastic spectral displacement. Vulnerability curves are developed based on inelastic spectral displacement. The study shows that inelastic spectral displacement reflects the impact of different frequency components with periods larger than fundamental period on inelastic structural response. The damage potential of ground motion on structures with fundamental period prolonging caused by structural soften can be caught by inelastic spectral displacement. To be compared with elastic spectral displacement, inelastic spectral displacement is a more sufficient and efficient intensity measure, which reduces the uncertainty of vulnerability analysis and the impact of input ground motion selection on vulnerability analysis result.

Keywords: vulnerability, probability seismic demand analysis, ground motion intensity measure, sufficiency, efficiency, inelastic time history analysis

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Authors: Mohammad Ahmad, Dayalan Kasilingam

Abstract:

In this paper, a spectral domain implementation of the fast multipole method is presented. It is shown that the aggregation, translation, and disaggregation stages of the fast multipole method (FMM) can be performed using the spectral domain (SD) analysis. The spectral domain fast multipole method (SD-FMM) has the advantage of eliminating the near field/far field classification used in conventional FMM formulation. The study focuses on the application of SD-FMM to one-dimensional (1D) and two-dimensional (2D) electric field integral equation (EFIE). The case of perfectly conducting strip, circular and square cylinders are numerically analyzed and compared with the results from the standard method of moments (MoM).

Keywords: electric field integral equation, fast multipole method, method of moments, wave scattering, spectral domain

Procedia PDF Downloads 407

Authors: A. Manallah, C. Meier

Abstract:

Confocal spectral interferometry (CSI) is an innovative optical method for determining microtopography of surfaces and thickness of transparent layers, based on the combination of two optical principles: confocal imaging, and spectral interferometry. Confocal optical system images at each instant a single point of the sample. The whole surface is reconstructed by plan scanning. The interference signal generated by mixing two white-light beams is analyzed using a spectrometer. In this work, five ‘rugotests’ of known standard roughnesses are investigated. The topography is then measured and illustrated, and the equivalent roughness is determined and compared with the standard values.

Keywords: confocal spectral interferometry, nondestructive testing, optical metrology, surface topography, roughness

Procedia PDF Downloads 277