Results 191 to 200 of about 423,442 (225)

Reweighted Sparse Regression for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2016
Hyperspectral unmixing (HSU) plays an important role in hyperspectral image (HSI) analysis. Recently, the HSU method based on sparse regression has drawn much attention. This paper presents a new weighted sparse regression problem for HSU and proposes two iterative reweighted algorithms for solving this problem, where the weights used for the next ...
Cheng Yong Zheng, Hong Li, Qiong Wang, C. L. Philip Chen   +3 more
openaire   +1 more source

Robust sparse unmixing of hyperspectral data

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Sparse unmixing (SU) of hyperspectral data has recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (
Yong Ma, Chang Li, Jiayi Ma
openaire   +1 more source

Superpixel-Based Graph Laplacian Regularized and Weighted Robust Sparse Unmixing

IEEE Transactions on Geoscience and Remote Sensing
The sparse unmixing (SU) technique is widely used in hyperspectral image (HSI) unmixing because it does not need to estimate the number of pure endmembers but directly obtains the spectra from known spectral libraries to construct the endmember matrix ...
Xin Zou, Mingming Xu, Shanwei Liu, Hui Sheng   +3 more
semanticscholar   +1 more source

Sparse Unmixing in the Presence of Mixed Noise Using ℓ0-Norm Constraint and Log-Cosh Loss

IEEE Transactions on Geoscience and Remote Sensing
Over the past two decades, sparse unmixing (SU) has gained significant attention in the realm of hyperspectral imaging. The aims of SU are to seek a subset of spectral signatures and estimate their fractional abundances to represent each mixed spectral ...
Yiu Yu Chan, Xiaopeng Li, Jiajie Mai, Chi-Sing Leung, Hing Cheung So   +4 more
semanticscholar   +1 more source

Sparse Dictionary Learning for Blind Hyperspectral Unmixing

IEEE Geoscience and Remote Sensing Letters, 2019
Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity.
Yang Liu, Yi Guo, Feng Li, Lei Xin, Puming Huang   +4 more
openaire   +2 more sources

Structured low-rank representation learning for hyperspectral sparse unmixing

International Journal of Remote Sensing
Sparse unmixing methods have been widely used to estimate the abundance of each material component from hyperspectral images. However, conventional sparse unmixing approaches only consider matrix factorization without limited explorations on the high ...
Jian Zhang, Hongsong Dong, Wenlian Gao, Li Zhang, Zhiwen Xue, Xiangfei Shen   +5 more
semanticscholar   +1 more source

Cycle-Consistent Sparse Unmixing Network Based on Deep Image Prior

IEEE International Geoscience and Remote Sensing Symposium
A cycle-consistent sparse unmixing network based on deep image prior (C2SU-DIP) is proposed in this paper, to reduce the complexity of sparse unmixing (SU) algorithm and the loss of details in hyperspectral images (HSIs) simultaneously.
Yifan Zhang, Chaoqun Dong, Shaohui Mei
semanticscholar   +1 more source

Hyperspectral image sparse unmixing with non-convex penalties

J. Electronic Imaging
. Sparse unmixing plays an important role in hyperspectral image analysis due to its ability to sparsely estimate abundances with a potentially large-scale spectral library.
Jun Lv, Kai Liu
semanticscholar   +1 more source

DSSU: Dual-Stage Sparse Unmixing for Asynchronous Mixed Signal of Infrared Targets

IEEE Transactions on Geoscience and Remote Sensing
The unmixing of infrared mixed targets is the foundation for long-range cluster target recognition and scale estimation. In this article, a dual-stage sparse unmixing (DSSU) method is proposed for asynchronous mixed infrared signals with high ...
Kewen Huang, Linhui Li, Hongxia Mao, Bo Yang, Yan Tian   +4 more
semanticscholar   +1 more source

Parallel method for sparse semisupervised hyperspectral unmixing

SPIE Proceedings, 2013
Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance’s physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to ...
Nascimento, Jose, Rodríguez Alves, José M., Plaza, Antonio, Silva, Vítor, Bioucas-Dias, José M.   +4 more
openaire   +2 more sources