Results 181 to 190 of about 423,442 (225)

Sparse and low rank hyperspectral unmixing

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
openaire   +1 more source

Interpretable Low-Rank Sparse Unmixing and Spatial Attention-Enhanced Difference Mapping Network for Hyperspectral Change Detection

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral change detection is a key technique for recognizing differences in hyperspectral images (HSIs) of the same location over two or more periods of time, widely used in the fields of environmental monitoring, resource management, and urban ...
Qingran Cai, Jiahui Qu, Wenqian Dong, Yufei Yang   +3 more
semanticscholar   +1 more source

Local Sparsity Blocks and Tensor Low Rank Regularized Sparse Unmixing

Workshop on Hyperspectral Image and Signal Processing, 2023
Hyperspectral unmixing has been an important technique in remote sensing application. Sparse unmixing is a type of widely used methods, which assumes the endmembers in a hyperspectral image (HSI) are relatively small portion of the spectral library.
Xinru Jiang, Lei Sun, Peizeng Lin
semanticscholar   +1 more source

Multiscale Spatial Graph-Regularized Hierarchical Sparse Unmixing Based on the Framelet Transform

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral unmixing (HU) is dedicated to disassemble mixed pixels into a group of pure spectral signatures (endmembers) and their respective fractional abundances.
Shaoquan Zhang, Yuyang Liu, Fan Li, Jiajun Zheng, Lianghui Liang, A. Plaza, Chengzhi Deng, Shengqian Wang   +7 more
semanticscholar   +1 more source

Collaborative sparse unmixing of hyperspectral data

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in a large collection of pure spectral signatures (spectral library), known a priori.
Marian-Daniel Iordache, Jose M. Bioucas-Dias, Antonio Plaza   +2 more
openaire   +1 more source

Hyperspectral Sparse Unmixing Based on Dual-Population Cooperative Optimization

IEEE Geoscience and Remote Sensing Letters
Hyperspectral unmixing is an essential technique for analyzing the Earth’s surface. This letter proposes a sparse unmixing method based on a dual-population genetic algorithm (DPGA) for the highly correlated spectral library.
Kewen Huang, Luoyang Chen, Yichun Dong, Yan Tian   +3 more
semanticscholar   +1 more source

A Nonconvex Framework for Sparse Unmixing Incorporating the Group Structure of the Spectral Library

IEEE Transactions on Geoscience and Remote Sensing, 2022
Sparse unmixing (SU) has been widely investigated for hyperspectral analysis with the aim to find the optimal subset of spectral signatures in a spectral library (known in advance) that can optimally model each pixel of the given hyperspectral image ...
Longfei Ren, Zheng Ma, F. Bovolo, L. Bruzzone   +3 more
semanticscholar   +1 more source

Parallel sparse unmixing of hyperspectral data

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction ...
Alves, José M. Rodriguez, Nascimento, Jose, Bioucas-Dias, José M., Plaza, António, Silva, Vítor   +4 more
openaire   +2 more sources

Multiscale Spatial Sparse Unmixing for Remotely Sensed Hyperspectral Imagery

IEEE International Geoscience and Remote Sensing Symposium, 2023
Spectral unmixing is a crucial aspect of hyperspectral image processing. Given the low spatial resolution of hyperspectral remote sensing sensors, combined with the complexity and diversity of actual ground objects, hyperspectral remote sensing images ...
Jiajun Zheng, Huqing Liang, Shaoquan Zhang, Fan Li, Pengfei Lai, Shengqian Wang, Chengzhi Deng   +6 more
semanticscholar   +1 more source

A greedy algorithm for sparse unmixing

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Hyperspectral imaging sensors provide image data containing both spatial and detailed spectral information. However, due to low spatial resolution, the pixels in hyperspectral images are actually mixtures of the spectral signatures of the materials. Sparse unmixing assumes that these mixed pixels are sparse linear combinations of different material ...
Kemal Gurkan Toker, Seniha Esen Yuksel
openaire   +1 more source