Efficient tensor decomposition approach for estimation of the number of endmembers in a hyperspectral image-Reference-Cited by-同舟云学术

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Efficient tensor decomposition approach for estimation of the number of endmembers in a hyperspectral image

Published:2020-03-31 Issue:01 Volume:14 Page:1
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:
Short-container-title:J. Appl. Rem. Sens.

Author:

Das Samiran¹,Routray Aurobinda²,Deb Alok Kanti²

Affiliation:

1. Indian Institute of Technology Kharagpur, Advanced Technology Development Center, Kharagpur, West Be

2. Indian Institute of Technology Kharagpur, Department of Electrical Engineering, Kharagpur, West Beng

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition;Journal of Applied Remote Sensing;2023-11-30

2. Hyperspectral Unmixing by Convolutional Auto-encoder with Deep Subspace Clustering and Cadidate Pixel Selection;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

3. Spatial Validation of Spectral Unmixing Results: A Systematic Review;Remote Sensing;2023-05-29

4. Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A comprehensive review;IEEE Geoscience and Remote Sensing Magazine;2023-03

5. Sparsity Regularized Deep Subspace Clustering for Multicriterion-Based Hyperspectral Band Selection;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2022

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