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Sparse filtering based hyperspectral unmixing
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016This work proposes a hyperspectral unmixing technique based on sparse filtering approach. The proposed method exploits the sparsity of feature distribution rather than modeling the data distribution. The proposed sparse filtering based unmixing procedure is essentially parameter-free, and the only parameter is to find the number of endmembers to be ...
Hemant Kumar Aggarwal, Angshul Majumdar
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Window Transformer Convolutional Autoencoder for Hyperspectral Sparse Unmixing
IEEE Geoscience and Remote Sensing Letters, 2023The availability of spectral library makes hyperspectral sparse unmixing an attractive unmixing scheme, and the powerful feature extraction capability of deep learning meets the requirements of estimating abundances with hundreds of channels in sparse ...
Fanqiang Kong +4 more
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Framelet-Based Sparse Unmixing of Hyperspectral Images
IEEE Transactions on Image Processing, 2016Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing.
Guixu, Zhang, Yingying, Xu, Faming, Fang
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Graph learning and denoising-based weighted sparse unmixing for hyperspectral images
International Journal of Remote Sensing, 2023Sparse unmixing is a semisupervised unmixing method based on the linear mixture model, in which the spectral library is known a prior, and has received considerable attention recently.
Fu-Xin Song, S. Deng
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Improving the performance of sparse unmixing
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Sparse unmixing has been proposed for hyperspectral image analysis. It has been shown that improved performance can be achieved when endmembers from a spectral library are used. However, when endmembers from image data have to be employed for unmixing, such a sparse-constrained approach may be problematic due to the fact that endmembers are generally ...
Qian Du, Ben Ma, Nareenart Raksuntorn
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Robust Sparse Unmixing for Hyperspectral Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2018A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library.
Dan Wang, Zhenwei Shi, Xinrui Cui
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Spectral-spatial-sparse unmixing with superpixel-oriented graph Laplacian
International Journal of Remote Sensing, 2023Sparse unmixing has made great progress in hyperspectral unmixing recently. To improve the unmixing accuracy, spatial information has been widely added to the unmixing model.
Zhi Li, Ruyi Feng, Lizhe Wang, T. Zeng
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Sparse Unmixing of Hyperspectral Images With Noise Reduction Using Spatial Filtering
IEEE Transactions on Instrumentation and MeasurementSparse unmixing has emerged as a powerful technique for addressing the presence of mixed pixels in hyperspectral images. A commonly employed approach involves integrating spectral information and spatial features within a sparse unmixing framework, with ...
Shaoquan Zhang +7 more
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Robust Sparse Unmixing via Continuous Mixed Norm to Address Mixed Noise
IEEE Geoscience and Remote Sensing LettersSparse unmixing, a critical task in hyperspectral image interpretation, aims to identify an optimal subset of endmembers from a predefined library and estimate the fractional abundances for each pixel.
Jincheng Gao, Jiayu Shi, Fei Zhu
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IEEE Transactions on Geoscience and Remote Sensing, 2023
As the spectral library continues to expand, sparse hyperspectral unmixing methods have been developed to solve the mixing problem without the need for end-member extraction or generation.
Bingkun Liang +8 more
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As the spectral library continues to expand, sparse hyperspectral unmixing methods have been developed to solve the mixing problem without the need for end-member extraction or generation.
Bingkun Liang +8 more
semanticscholar +1 more source