Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071778
Sujan Singh Niranjan, Neelima Chaube, J. Sarup
This paper presents the study of mineral and vegetation in explored fields around the San Juan coal mines west of Farmington, New Mexico. The purpose of this research work is to map the mineral rock & vegetation for statistically analyzing the study area. Pre-processing of Hyperspectral imagery (HSI) data is required for conversion from digital value to reflectance. Minimum Noise Fraction (MNF) and Pure Pixel Index (PPI) method is used for extraction of Endmember fraction. Spectral signature matching procedure is done with U.S. Geological Survey (USGS) Spectral library, which contain spectra of individual species that have been acquired at test sites representatives of varied terrain and climatic zones, observed in the field under natural conditions. Spectral Angle Mapper (SAM) technique is used for spectral analysis and mapping of image. Finally study area is mapped in two classes namely Carnallite mineral and Sagebrush vegetation plants. Land covered by Sagebrush plant is 8.31% and Carnallite is 1.41% of study area.
{"title":"Mapping of the carnallite mineral and sagebrush vegetation plant by using hyperspectral remote sensing and usgs spectral library","authors":"Sujan Singh Niranjan, Neelima Chaube, J. Sarup","doi":"10.1109/WHISPERS.2016.8071778","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071778","url":null,"abstract":"This paper presents the study of mineral and vegetation in explored fields around the San Juan coal mines west of Farmington, New Mexico. The purpose of this research work is to map the mineral rock & vegetation for statistically analyzing the study area. Pre-processing of Hyperspectral imagery (HSI) data is required for conversion from digital value to reflectance. Minimum Noise Fraction (MNF) and Pure Pixel Index (PPI) method is used for extraction of Endmember fraction. Spectral signature matching procedure is done with U.S. Geological Survey (USGS) Spectral library, which contain spectra of individual species that have been acquired at test sites representatives of varied terrain and climatic zones, observed in the field under natural conditions. Spectral Angle Mapper (SAM) technique is used for spectral analysis and mapping of image. Finally study area is mapped in two classes namely Carnallite mineral and Sagebrush vegetation plants. Land covered by Sagebrush plant is 8.31% and Carnallite is 1.41% of study area.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129131889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071745
M. Colom, G. Blanchet, A. Klonecki, O. Lezeaux, E. Pequignot, F. Poustomis, C. Thiebaut, S. Ythier, J. Morel
We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR.
{"title":"BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images","authors":"M. Colom, G. Blanchet, A. Klonecki, O. Lezeaux, E. Pequignot, F. Poustomis, C. Thiebaut, S. Ythier, J. Morel","doi":"10.1109/WHISPERS.2016.8071745","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071745","url":null,"abstract":"We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130739617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071663
P. V. Arun, K. Buddhiraju, A. Porwal
In this paper, we investigate the use of coarse image features for predicting class label distributions at a finer scale. The major contributions of this work are 1) use of coarse image features to improve the optimization formulation of conventional rank based approaches 2) use of inter class compatibility information from coarse images to refine the predicted target distribution 3) an enhanced unsupervised variogram based sub-pixel mapping approach 4) inclusion of abundance estimation uncertainty in the unmixing process. The proposed modifications on rank based and variogram based approaches have produced an accuracy improvement of 10–15%. The sensitivities of these approaches towards tunable parameters are also analyzed.
{"title":"Integration of contextual knowledge in unsupervised sub-pixel classification","authors":"P. V. Arun, K. Buddhiraju, A. Porwal","doi":"10.1109/WHISPERS.2016.8071663","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071663","url":null,"abstract":"In this paper, we investigate the use of coarse image features for predicting class label distributions at a finer scale. The major contributions of this work are 1) use of coarse image features to improve the optimization formulation of conventional rank based approaches 2) use of inter class compatibility information from coarse images to refine the predicted target distribution 3) an enhanced unsupervised variogram based sub-pixel mapping approach 4) inclusion of abundance estimation uncertainty in the unmixing process. The proposed modifications on rank based and variogram based approaches have produced an accuracy improvement of 10–15%. The sensitivities of these approaches towards tunable parameters are also analyzed.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116567482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071704
A. Saranathan, M. Parente
Hyperspectral images often contain multiple intimate (nonlinear) mixtures. When attempting to unmix such datasets it is important to identify (cluster) the different mixtures present in the data and also minimize the effects of the nonlinearities in the data due to intimate mixing (embedding). Manifold clustering and embedding techniques appear to be an ideal tool for this task. Previous work in the field of manifold clustering either make simplifying assumptions or trade-off the embedding objective to improve the clustering. This is unacceptable in the case of unmixing as the embedded data is used for future processing (for e.g. abundance estimation). We discuss a low rank neighborhood representation which expresses each point as an affine combination of its neighbors on the same manifold. This ensures that the reconstruction matrix has a block diagonal structure, enabling the identification of classes by spectral clustering. The embedding of the different manifolds can also be obtained from this matrix. We will show the improved performance of this algorithm on simulated as well as real hyperspectral reflectance data of two ternary mixtures with two shared endmembers.
{"title":"Unmixing multiple intimate mixtures via a locally low-rank representation","authors":"A. Saranathan, M. Parente","doi":"10.1109/WHISPERS.2016.8071704","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071704","url":null,"abstract":"Hyperspectral images often contain multiple intimate (nonlinear) mixtures. When attempting to unmix such datasets it is important to identify (cluster) the different mixtures present in the data and also minimize the effects of the nonlinearities in the data due to intimate mixing (embedding). Manifold clustering and embedding techniques appear to be an ideal tool for this task. Previous work in the field of manifold clustering either make simplifying assumptions or trade-off the embedding objective to improve the clustering. This is unacceptable in the case of unmixing as the embedded data is used for future processing (for e.g. abundance estimation). We discuss a low rank neighborhood representation which expresses each point as an affine combination of its neighbors on the same manifold. This ensures that the reconstruction matrix has a block diagonal structure, enabling the identification of classes by spectral clustering. The embedding of the different manifolds can also be obtained from this matrix. We will show the improved performance of this algorithm on simulated as well as real hyperspectral reflectance data of two ternary mixtures with two shared endmembers.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130987624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071783
Shirin Hassanzadeh, A. Karami, Rob Heylen, P. Scheunders
In this paper, a new lossy compression method for hyperspectral images (HSI) is introduced based on the NonNegative Tucker Decomposition (NTD). HSI are considered as a 3D dataset: two spatial dimensions and one spectral dimension. The NTD algorithm decomposes the original data into a smaller 3D dataset (core tensor) and three matrices. In the proposed method, in order to find the optimal decomposition, the Block Coordinate Descent (BCD) method is used, which is initialized by using Compressed Sensing (CS). The obtained optimal core tensor and matrices are coded by applying arithmetic coding and finally the compressed dataset is transmitted. The proposed method is applied to real datasets. Our experimental results show that, in comparison with state-of-the-art lossy compression methods, the proposed method achieves the highest signal to noise ratio (SNR) at any desired compression ratio (CR) while noise reduction is simultaneously obtained.
{"title":"Compression of hyperspectral images using block coordinate descent search and compressed sensing","authors":"Shirin Hassanzadeh, A. Karami, Rob Heylen, P. Scheunders","doi":"10.1109/WHISPERS.2016.8071783","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071783","url":null,"abstract":"In this paper, a new lossy compression method for hyperspectral images (HSI) is introduced based on the NonNegative Tucker Decomposition (NTD). HSI are considered as a 3D dataset: two spatial dimensions and one spectral dimension. The NTD algorithm decomposes the original data into a smaller 3D dataset (core tensor) and three matrices. In the proposed method, in order to find the optimal decomposition, the Block Coordinate Descent (BCD) method is used, which is initialized by using Compressed Sensing (CS). The obtained optimal core tensor and matrices are coded by applying arithmetic coding and finally the compressed dataset is transmitted. The proposed method is applied to real datasets. Our experimental results show that, in comparison with state-of-the-art lossy compression methods, the proposed method achieves the highest signal to noise ratio (SNR) at any desired compression ratio (CR) while noise reduction is simultaneously obtained.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134538629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071752
Jingliang Hu, Pedram Ghamisi, A. Schmitt, Xiaoxiang Zhu
In this paper, we propose an object-based fusion approach for the joint use of polarimetric synthetic aperture radar (PolSAR) and hyperspectral data. The proposed approach extracts information from both datasets based on an object-level, which is used here for land use classification. The achieved classification result infers that the proposed methodology improves the classification performance of both hyperspectral and PolSAR data and can properly gather complementary information of the two kinds of dataset. The fusion approach also considers that only limited training samples are available, which is often the case in remote sensing.
{"title":"Object based fusion of polarimetric SAR and hyperspectral imaging for land use classification","authors":"Jingliang Hu, Pedram Ghamisi, A. Schmitt, Xiaoxiang Zhu","doi":"10.1109/WHISPERS.2016.8071752","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071752","url":null,"abstract":"In this paper, we propose an object-based fusion approach for the joint use of polarimetric synthetic aperture radar (PolSAR) and hyperspectral data. The proposed approach extracts information from both datasets based on an object-level, which is used here for land use classification. The achieved classification result infers that the proposed methodology improves the classification performance of both hyperspectral and PolSAR data and can properly gather complementary information of the two kinds of dataset. The fusion approach also considers that only limited training samples are available, which is often the case in remote sensing.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133864817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071674
Jie Li, C. Qi, Jingping Zhu, Wenzi Liao, W. Philips
Hyperspectral imaging technique has been widely used in remote sensing, surveillance in agriculture, environmental monitoring, etc. However, spectral information alone cannot well discriminate objects made with the same material. Conventional methods either fuse complementary information from other sensors or mine relevant information from the original hyperspectral data to improve the recognition rations. It may increase the cost and reduce the efficiency. In this paper, we propose an easier alternative approach: we present the prototype of a compact static Fourier transform hyperspectral imaging polarimeter, which couples hyperspectraland polarization imaging in a unified instrument and allows better material discrimination. The instrument, which is formed by cascading two crystal retarders and a birefringent interferometer, offers significant advantages over traditional implementations. Specifically, without any internal moving parts or electronics controllable elements, the spectrum, full wavelength dependent polarization and spatial information of a scene can be acquired simultaneously. Principles and experimental results in a case study are encouraging.
{"title":"Static Fourier transform hyperspectral imaging polarimeter","authors":"Jie Li, C. Qi, Jingping Zhu, Wenzi Liao, W. Philips","doi":"10.1109/WHISPERS.2016.8071674","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071674","url":null,"abstract":"Hyperspectral imaging technique has been widely used in remote sensing, surveillance in agriculture, environmental monitoring, etc. However, spectral information alone cannot well discriminate objects made with the same material. Conventional methods either fuse complementary information from other sensors or mine relevant information from the original hyperspectral data to improve the recognition rations. It may increase the cost and reduce the efficiency. In this paper, we propose an easier alternative approach: we present the prototype of a compact static Fourier transform hyperspectral imaging polarimeter, which couples hyperspectraland polarization imaging in a unified instrument and allows better material discrimination. The instrument, which is formed by cascading two crystal retarders and a birefringent interferometer, offers significant advantages over traditional implementations. Specifically, without any internal moving parts or electronics controllable elements, the spectrum, full wavelength dependent polarization and spatial information of a scene can be acquired simultaneously. Principles and experimental results in a case study are encouraging.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128675375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071790
Lifu Zhang
A new software have been developed for multidimensional analysis for remote sensing application. A new data storage structure named “mdd” for storing long time series remotely sensed data with spatial, temporal and spectral dimensions was induced as well as. Five data formats were included within the multidimensional data storage, which were TSB, TSP, TIB, TIP and TIS. MARS can be used for building multidimensional datasets and extracting information from MDD data file for time-series analysis. This paper introduced the main functions of MARS software by using MODIS long time series data as an example. MARS have the potential on analyzing long time satellite datasets.
{"title":"Development of multidimensional analysis of remote sensing (MARS) software","authors":"Lifu Zhang","doi":"10.1109/WHISPERS.2016.8071790","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071790","url":null,"abstract":"A new software have been developed for multidimensional analysis for remote sensing application. A new data storage structure named “mdd” for storing long time series remotely sensed data with spatial, temporal and spectral dimensions was induced as well as. Five data formats were included within the multidimensional data storage, which were TSB, TSP, TIB, TIP and TIS. MARS can be used for building multidimensional datasets and extracting information from MDD data file for time-series analysis. This paper introduced the main functions of MARS software by using MODIS long time series data as an example. MARS have the potential on analyzing long time satellite datasets.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1999 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116921266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071722
Daniele Picone, R. Restaino, G. Vivone, P. Addesso, M. Mura, J. Chanussot
The sharpening of hyperspectral (HS) images introduces novel questions that have never been faced by classical pansharpening, which deals with the fusion of multispectral and panchromatic images. In this paper, we focus on the fusion of high resolution MultiSpectral (MS) and low resolution HS data, namely tackling the problem of assigning the optimal MS channel for each HS band through the minimization of the Spectral Angle Mapper (SAM) metric. The performance is assessed on two datasets, both composed by a HS and a MS image acquired by the Hyperion and the ALI sensors, respectively. Several MultiResolution Analysis pansharpening approaches are used for evaluating the performance improvements with respect to existing methods.
{"title":"Multispectral and hyperspectral data fusion based on SAM minimization band assignment approach","authors":"Daniele Picone, R. Restaino, G. Vivone, P. Addesso, M. Mura, J. Chanussot","doi":"10.1109/WHISPERS.2016.8071722","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071722","url":null,"abstract":"The sharpening of hyperspectral (HS) images introduces novel questions that have never been faced by classical pansharpening, which deals with the fusion of multispectral and panchromatic images. In this paper, we focus on the fusion of high resolution MultiSpectral (MS) and low resolution HS data, namely tackling the problem of assigning the optimal MS channel for each HS band through the minimization of the Spectral Angle Mapper (SAM) metric. The performance is assessed on two datasets, both composed by a HS and a MS image acquired by the Hyperion and the ALI sensors, respectively. Several MultiResolution Analysis pansharpening approaches are used for evaluating the performance improvements with respect to existing methods.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117051721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071687
A. Karami, Rob Heylen, P. Scheunders
In this paper, we propose a new denoising method based on a 2D non-subsampled shearlet transform (NSST) and fully constrained least squares unmixing (FCLSU). In the proposed method, first low noisy (LN) bands are separated from high noisy (HN) bands using spectral correlation. Second, NSST is applied to each spectral band of the hyperspectral images. Third, LN bands are denoised using a thresholding technique on the shearlet coefficients and HN bands are denoised by applying FCLSU. The proposed method is compared to state of the art denoising methods on synthetic and real hyperspectral datasets. The effect of denoising on classification accuracy is also investigated. Obtained results show the superiority of the proposed approach.
{"title":"Denoising of hyperspectral images using shearlet transform and fully constrained least squares unmixing","authors":"A. Karami, Rob Heylen, P. Scheunders","doi":"10.1109/WHISPERS.2016.8071687","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071687","url":null,"abstract":"In this paper, we propose a new denoising method based on a 2D non-subsampled shearlet transform (NSST) and fully constrained least squares unmixing (FCLSU). In the proposed method, first low noisy (LN) bands are separated from high noisy (HN) bands using spectral correlation. Second, NSST is applied to each spectral band of the hyperspectral images. Third, LN bands are denoised using a thresholding technique on the shearlet coefficients and HN bands are denoised by applying FCLSU. The proposed method is compared to state of the art denoising methods on synthetic and real hyperspectral datasets. The effect of denoising on classification accuracy is also investigated. Obtained results show the superiority of the proposed approach.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114531470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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