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Unsupervised spectral sub-feature learning for hyperspectral image classification

Viktor Slavkovikj, Steven Verstockt, Wesley De Neve, Sofie Van Hoecke, Rik Van de Walle
2016 International Journal of Remote Sensing  
In this article, we propose an unsupervised feature learning method for classification of hyperspectral images.  ...  Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis.  ...  We compare two different unsupervised learning algorithms for learning an over-complete dictionary: one based on a sparse modelling approach and the other on an efficient SGD k-means algorithm.  ... 
doi:10.1080/01431161.2015.1125554 fatcat:ef6tvu4qi5c3vnbqnygpe3rg6a

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Weiwei Sun, Man Jiang, Weiyue Li, Yinnian Liu
2016 Remote Sensing  
A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification.  ...  Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications.  ...  The SSC based methods combine the sparse coding model with the subspace clustering approach, and the benefit of clustering renders that the achieved band subset is easy to interpret.  ... 
doi:10.3390/rs8030238 fatcat:ouey7tc2bvc37gjtlrjuwazdaa

A survey of band selection techniques for hyperspectral image classification

Shrutika Sawant, Manoharan Prabukumar
2020 Journal of Spectral Imaging  
Our purpose is to highlight the progress attained in band selection techniques for hyperspectral image classification and to identify possible avenues for future work, in order to achieve better performance  ...  Focusing on the classification task, this article provides an extensive and comprehensive survey on band selection techniques describing the categorisation of methods, methodology used, different searching  ...  Acknowledgement The authors thank the Council of Scientific & Industrial Research (CSIR), New Delhi, India, for the award of CSIR-SRF and the Vellore Institute of Technology (VIT) for providing a VIT seed  ... 
doi:10.1255/jsi.2020.a5 fatcat:cvibjoofbbd6jpu4ij626wigdy

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  selection [19] , i.e., select only a subset of most significant bands for analysis.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Regularized Sparse Band Selection via Learned Pairwise Agreement

Zhixi Feng, Shuyuan Yang, Xiaolong Wei, Quanwei Gao, Licheng Jiao
2020 IEEE Access  
Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned  ...  The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement  ...  A few clustering based band selection techniques for hyperspectral images exist in the literature e.g., Ward's linkage strategy using divergence (WaLuDi) [19] , or using mutual information (WaLuMI) [  ... 
doi:10.1109/access.2020.2971556 fatcat:72u7qlvq75cpnatvv773xlpxcq

Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features

Yuntao Qian, Minchao Ye, Jun Zhou
2013 IEEE Transactions on Geoscience and Remote Sensing  
After feature extraction step, sparse representation/modeling is applied for data analysis and processing via sparse regularized optimization, which selects a small subset of the original feature variables  ...  In this paper, we propose a hyperspectral feature extraction and pixel classification method based on structured sparse logistic regression and three-dimensional discrete wavelet transform (3D-DWT) texture  ...  MIXING LINEAR SPARSE MODELS FOR NONLINEAR CLASSIFICATION A.  ... 
doi:10.1109/tgrs.2012.2209657 fatcat:t2jc33vmanhihfy7wwcm25wb6m

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Pedram Ghamisi, Naoto Yokoya, Jun Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, Antonio Plaza
2017 IEEE Geoscience and Remote Sensing Magazine  
This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality  ...  Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide.  ...  In addition, the authors would like to thank the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI Houston data set, and the IEEE GRSS Image Analysis  ... 
doi:10.1109/mgrs.2017.2762087 fatcat:6ezzye7yyvacbouduqv2f2c7gi

Band Selection using Combined Divergence-Correlation Index and Sparse Loadings representation for Hyperspectral Image Classification

Munmun Baisantry, Anil Kumar Sao, Dericks Praise Shukla
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Experimental results indicate that the proposed method can proficiently select a set of distinct and discriminative bands, which can help in effective hyperspectral classification.  ...  In this article, a feature extraction-based, clustering-ranking type band selection method is proposed in which the band selection is performed in two stages.  ...  Section IV presents the feature extraction-based clustering-ranking type band selection framework using loadings-based SSC and a new metric called CDCI for hyperspectral image classification.  ... 
doi:10.1109/jstars.2020.3014784 fatcat:7yhkrustpjfjhpih6nb75yz4ae

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 Hu, C., Zhang,  ...  Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T., Wu, Y., Zheng, G., Zhang, D., Zhang, Y., and Li, Y.,  ...  ., +, TGRS Oct. 2019 7352-7364 Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach

2017 Remote Sensing  
Thus, this paper presents a hybrid approach consisting of spectral-spatial feature sparse representation (SS-SR) and post-processing to extract urban impervious surface from hyperspectral images.  ...  The improvement is more significant when combining SS-SR with post-classification approach.  ...  In this paper, we propose a hybrid method for extracting impervious surface from hyperspectral images based on spectral-spatial sparse representation and post-classification.  ... 
doi:10.3390/rs9050456 fatcat:czfpylzugzalngt6534glzu4py

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Tatireddy Reddy, Jonnadula Harikiran
2022 Journal of Spectral Imaging  
Hyperspectral imaging is used in a wide range of applications.  ...  Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.  ...  Band selection methods choose the original spectral band's subset that should contain the image's most essential features.  ... 
doi:10.1255/jsi.2022.a1 fatcat:rue5klkmlfcrzftepc6lzfcbfe

Unfolding the Restrained Encountered in Hyperspectral Images

2019 International journal of recent technology and engineering  
For a single scene, the hyperspectral images (HSI) are composed of hundreds of channels of spectral data.  ...  For different materials, with the availability of detailed spectral information, hundreds of contracted bands are collected by hyperspectral sensors.  ...  Again as table 2 depicts one of the approaches to support the pixel-wise classification based on the sparse representation classifier where it outperforms the traditional classifiers. TABLE II.  ... 
doi:10.35940/ijrte.b1763.078219 fatcat:cgdtvtrzbzaylm4eowtog52x3m

Hierarchical Sparse Learning with Spectral-Spatial Information for Hyperspectral Imagery Denoising

Shuai Liu, Licheng Jiao, Shuyuan Yang
2016 Sensors  
Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset.  ...  Notably, the works based on sparse dictionary learning have proven their efficacy and popularity for image recovery.  ...  (a) (b) (c) Based on the curves in Figure 2 , it can be found that the correlation coefficients across adjacent bands obviously vary considerably for different hyperspectral images.  ... 
doi:10.3390/s16101718 pmid:27763511 pmcid:PMC5087505 fatcat:u3fj3oxqoza57lq5zlv4twmy2a

Hyperspectral Band Selection Using Attention-based Convolutional Neural Networks

Pablo Ribalta Lorenzo, Lukasz Tulczyjew, Michal Marcinkiewicz, Jakub Nalepa
2020 IEEE Access  
In this paper, we introduce a novel algorithm for hyperspectral band selection that couples new attention-based convolutional neural networks used to weight the bands according to their importance with  ...  They are built upon the observation that for a vast number of applications only a subset of all bands convey the important information about the underlying material, hence we can safely decrease the data  ...  ACKNOWLEDGMENT The authors are grateful to the anonymous reviewers for their constructive and valuable comments that helped improve the article.  ... 
doi:10.1109/access.2020.2977454 fatcat:r5wchsbi6rad5p6ppjn767ckd4

Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition [article]

Zohaib Khan
2014 arXiv   pre-print
It was found that the joint sparse and joint group sparse hyperspectral image models achieve lower reconstruction error and higher recognition accuracy using only a small subset of bands.  ...  This thesis proposes spatio-spectral techniques for hyperspectral image analysis.  ...  A test hyperspectral image cube is compressively sensed (reconstructed by learned model) and used for classification.  ... 
arXiv:1407.7686v1 fatcat:cdx3eaoyvjaf7d6ve56c7ohv54
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