Results 11 to 20 of about 309,259 (214)
Hyperspectral Unmixing with Robust Collaborative Sparse Regression [PDF]
Remote Sensing, 2016 Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear ...
Chang Li +4 more
doaj +3 more sources
Sparse unmixing using deep convolutional networks
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022 : This paper proposes a sparse unmixing technique using a convolutional neural network (SUnCNN). We reformulate the sparse unmixing problem into an optimization over the parameters of a convolutional network.
Scheunders, Paul +2 more
core +3 more sources
Deblurring and sparse unmixing for hyperspectral images
IEEE Transactions on Geoscience and Remote Sensing, 2013 The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators ...
Huang, Ting Zhu +4 more
core +2 more sources
Bilateral Joint-Sparse Regression for Hyperspectral Unmixing [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the same endmembers, which results in a few nonzero rows in the abundance ...
Jie Huang +4 more
doaj +3 more sources
Double reweighted sparse regression for hyperspectral unmixing
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016 Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing.
Heng-Chao Li +7 more
core +3 more sources
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation
Remote Sensing, 2017 Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different ...
Chang Li +5 more
doaj +2 more sources
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 +2 more sources
Manifold regularized sparse NMF for hyperspectral unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2013 Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions.
Wu, H. +8 more
core +3 more sources
Remote SensingIn recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in ...
Kaijun Yang +3 more
doaj +2 more sources
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.
Guo, Yi (R18457) +4 more
core +4 more sources