Results 21 to 30 of about 1,157 (179)
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
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Remote Sensing, 2019 Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
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A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu +4 more
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Simultaneous Nonconvex Denoising and Unmixing for Hyperspectral Imaging
IEEE Access, 2019 Sparse hyperspectral unmixing aims at finding the sparse fractional abundance vector of a spectral signature present in a mixed pixel. However, there are several types of noise present in the hyperspectral images.
Taner Ince, Tugcan Dundar
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Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (
Xiang Xu +3 more
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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
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TARGET TRANSFORMATION CONSTRAINED SPARSE UNMIXING (TTCSU) ALGORITHM FOR RETRIEVING HYDROUS MINERALS ON MARS: APPLICATION TO SOUTHWEST MELAS CHASMA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from hyperspectral images, a sparse unmixing algorithm has ...
H. Lin +6 more
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A Multiobjective Group Sparse Hyperspectral Unmixing Method With High Correlation Library
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral sparse unmixing aims at modeling pixels of hyperspectral image as a linear combination of a subset of a prior spectral library. Over the past years, spectral library has been constantly expanded, including spectra of the same material with ...
Yanyi Wei +4 more
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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
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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
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