Results 191 to 200 of about 309,259 (214)

Nonlocal similarity regularization for sparse hyperspectral unmixing

2014 IEEE Geoscience and Remote Sensing Symposium, 2014
This paper is concerned with semisupervised hyperspectral unmixing using a nonlocal similarity prior on the abundance images. To this end, the nonlocal self-similarity regularization is incorporated into the classical sparse regression formula to propose a new model for hyperspectral sparse unmixing.
Rui Wang 0090, Heng-Chao Li 0001
openaire   +1 more source

Local abundance regularization for hyperspectral sparse unmixing

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016
Hyperspectral sparse unmixing is a task to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. The abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region
Mia Rizkinia, Masahiro Okuda
openaire   +1 more source

Framelet-Based Sparse Unmixing of Hyperspectral Images

IEEE Transactions on Image Processing, 2016
Spectral 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
openaire   +3 more sources

Sparse hyperspectral unmixing with spatial discontinuity preservation

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
Sparse unmixing and sparse representation are known to be effective for improving the interpretation of remotely sensed hyperspectral images. Classic methods for incorporating spatial information into spectral unmixing assume that the abundances of the pixels are smooth and fall into a homogeneous region shared by the same endmembers and their ...
Shaoquan Zhang, Jun Li 0009, Zebin Wu 0001, Antonio Plaza   +3 more
openaire   +2 more sources

Sparse unmixing based denoising for hyperspectral images

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Until recently, hyperspectral image denoising was considered as a prior step to applications such as classification, detection, or unmixing. However, unmixing has been recently shown to also provide denoising due to its inherent property of representing pixels in terms of pure material signatures and their abundances. It is possible to eliminate sensor-
openaire   +2 more sources

Efficient Hyperspectral Sparse Regression Unmixing With Multilayers

IEEE Transactions on Geoscience and Remote Sensing, 2023
Xiangfei Shen, Lihui Chen 0002, Haijun Liu 0001, Xi Su, Wenjia Wei, Xia Zhu, Xichuan Zhou   +6 more
openaire   +2 more sources

Sparse Unmixing Based on Adaptive Loss Minimization

IEEE Transactions on Geoscience and Remote Sensing, 2022
Xinxin Zhang, Yuan Yuan 0001, Xuelong Li 0001   +2 more
openaire   +2 more sources

Superpixel-Based Reweighted Low-Rank and Total Variation Sparse Unmixing for Hyperspectral Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2021
Ruyi Feng, Liangpei Zhang, Yanfei Zhong
exaly  

Dual Spatial Weighted Sparse Hyperspectral Unmixing

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022
Yonggang Chen, Chengzhi Deng, Shaoquan Zhang, Fan Li, Ningyuan Zhang, Shengqian Wang   +5 more
openaire   +2 more sources

Spectral–Spatial-Weighted Multiview Collaborative Sparse Unmixing for Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2020
Xinbo Gao, Xinbo Gao
exaly