Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]
, 2012 Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Antonio Plaza +8 more
core +8 more sources
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing [PDF]
, 2015 This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images. Our contribution is twofold; first, we propose a new Bayesian model for structured sparse regression in which the supports of the ...
Altmann, Yoann +2 more
core +4 more sources
Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing
IEEE Access, 2019 Sparse unmixing has attracted much attention in recent years. It aims at estimating the fractional abundances of pure spectral signatures in mixed pixels in hyperspectral images.
Jie Huang +3 more
doaj +1 more source
IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 At present, an emerging technique called the algorithm unrolling approach has attracted wide attention, because it is capable of developing efficient and interpretable layers to eliminate the black-box nature of deep learning (DL).
Yuantian Shao, Qichao Liu, Liang Xiao
doaj +1 more source
Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery
Remote Sensing, 2018 Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the ...
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj +1 more source
Hyperspectral Unmixing Via Nonconvex Sparse and Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 In recent years, sparse unmixing has attracted significant attention, as it can effectively avoid the bottleneck problems associated with the absence of pure pixels and the estimation of the number of endmembers in hyperspectral scenes.
Hongwei Han +7 more
doaj +1 more source
Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Third-order tensors have been widely used in hyperspectral remote sensing because of their ability to maintain the 3-D structure of hyperspectral images.
Pan Zheng, Hongjun Su, Qian Du
doaj +1 more source
IEEE Access, 2021 Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation–based unmixing (CRU) for hyperspectral images is proposed.
Jing Wang
doaj +1 more source
Recent developments in sparse hyperspectral unmixing [PDF]
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010 This paper explores the applicability of new sparse algorithms to perform spectral unmixing of hyperspectral images using available spectral libraries instead of resorting to well-known endmember extraction techniques widely available in the literature.
Marian-Daniel Iordache +2 more
openaire +1 more source
Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation [PDF]
, 2019 In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors.
Chen, Xiuwan +4 more
core +1 more source