scholarly journals Semi-blind Unmixing of Hyperspectral Image by Eigenvalue based Virtual Dimensionality Estimation

2020 ◽  
Author(s):  
Samiran Das
Keyword(s):  
Hyperspectral Image ◽  
Spectral Library ◽  
New Approach ◽  
Virtual Dimensionality

This paper proposes a new approach to perform unmixing of hyperspectral image with the help of spectral library. The work introduces the concept of virtual dimensionality to unmixing

scholarly journals Semi-blind Unmixing of Hyperspectral Image by Eigenvalue based Virtual Dimensionality Estimation

2020 ◽  
Author(s):  
Samiran Das
Keyword(s):  
Hyperspectral Image ◽  
Spectral Library ◽  
New Approach ◽  
Virtual Dimensionality

This paper proposes a new approach to perform unmixing of hyperspectral image with the help of spectral library. The work introduces the concept of virtual dimensionality to unmixing

scholarly journals Evolutionary Multiobjective Optimization with Endmember Priori Strategy for Large-Scale Hyperspectral Sparse Unmixing

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2079
Author(s):  
Zhao Wang ◽  
Jinxin Wei ◽  
Jianzhao Li ◽  
Peng Li ◽  
Fei Xie
Keyword(s):  
Multiobjective Optimization ◽  
Large Scale ◽  
High Efficiency ◽  
Hyperspectral Image ◽  
Simulated Data ◽  
Reconstruction Error ◽  
Initial Population ◽  
Spectral Library ◽  
Nondominated Solutions ◽  
Mixed Pixels

Mixed pixels inevitably appear in the hyperspectral image due to the low resolution of the sensor and the mixing of ground objects. Sparse unmixing, as an emerging method to solve the problem of mixed pixels, has received extensive attention in recent years due to its robustness and high efficiency. In theory, sparse unmixing is essentially a multiobjective optimization problem. The sparse endmember term and the reconstruction error term can be regarded as two objectives to optimize simultaneously, and a series of nondominated solutions can be obtained as the final solution. However, the large-scale spectral library poses a challenge due to the high-dimensional number of spectra, it is difficult to accurately extract a few active endmembers and estimate their corresponding abundance from hundreds of spectral features. In order to solve this problem, we propose an evolutionary multiobjective hyperspectral sparse unmixing algorithm with endmember priori strategy (EMSU-EP) to solve the large-scale sparse unmixing problem. The single endmember in the spectral library is used to reconstruct the hyperspectral image, respectively, and the corresponding score of each endmember can be obtained. Then the endmember scores are used as a prior knowledge to guide the generation of the initial population and the new offspring. Finally, a series of nondominated solutions are obtained by the nondominated sorting and the crowding distances calculation. Experiments on two benchmark large-scale simulated data to demonstrate the effectiveness of the proposed algorithm.

Deep spectral convolution network for hyperspectral image unmixing with spectral library

2020 ◽  
Vol 176 ◽  
pp. 107672 ◽  
Author(s):  
Lin Qi ◽  
Jie Li ◽  
Ying Wang ◽  
Mingyu Lei ◽  
Xinbo Gao
Keyword(s):  
Hyperspectral Image ◽  
Spectral Library

scholarly journals Extended Morphological Profile Cube for Hyperspectral Image Classification

2021 ◽  
Author(s):  
ALOU DIAKITE ◽  
GUI JIANGSHENG ◽  
FU XIAPING
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Kappa Coefficient ◽  
Sparse Principal Component Analysis ◽  
Hyperspectral Image Classification ◽  
New Approach ◽  
Spatial Features ◽  
Training Samples

<p>Hyperspectral image (HSI) classification using convolutional neural network requires a lot of training samples, which is not always available. Consequently, decreases the classification accuracy due to the overfitting problem. Many studies have been conducted to solve the issue; however, they failed to solve it entirely. Therefore, we proposed a new approach to classify HSI with few training samples using a convolutional neural network in that context. The proposed approach employed an extended morphological profile cube (EMPC) to extract rich spectral-spatial features and then used a 3D densely connected network for classification. Besides, we used sparse principal component analysis to reduce the high spectral dimension of HSI. Experiments results on Indian Pines (IP) and University of Pavia (UP) datasets proved the efficiency of the proposed approach. It increased the OA by 2.61% - 13% and the Kappa coefficient by 2.68% - 15:51% on IP dataset and increased the OA by 0.17% - 11% and the Kappa coefficient by 0.23% - 19% on UP dataset, which is superior to some state-of-art methods.</p>

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