Adaptive spatial regularization for sequential unmixing of hyperspectral images

2021 ◽  
Author(s):  
Xiao Hong ◽  
Liu Hui
Keyword(s):  
Hyperspectral Images ◽  
Spatial Regularization

Spatial regularization for the unmixing of hyperspectral images

2015 ◽  
Author(s):  
Sebastian Bauer ◽  
Florian Neumann ◽  
Fernando Puente León
Keyword(s):  
Hyperspectral Images ◽  
Spatial Regularization

scholarly journals Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3662 ◽  
Author(s):  
Wenzheng Wang ◽  
Baojun Zhao ◽  
Fan Feng ◽  
Jinghong Nan ◽  
Cheng Li
Keyword(s):  
Anomaly Detection ◽  
Nonlinear Function ◽  
Detection Algorithm ◽  
Theoretical Explanation ◽  
Statistical Information ◽  
Hyperspectral Images ◽  
Hierarchical Framework ◽  
Anomaly Detector ◽  
Information Focus ◽  
Spatial Regularization

Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX’s output of each pixel is restrained by a nonlinear function and then imposed as a coefficient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods.

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Edge Preserving ◽  
Improvement Factor ◽  
Filter Output ◽  
Average Improvement ◽  
Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

Multi-temporal Hyperspectral Images Unmixing and Classification Based on 3D Signature Model and Matching

PIERS Online ◽  
2010 ◽  
Vol 6 (5) ◽  
pp. 480-484 ◽  
Author(s):  
Imed Riadh Farah ◽  
Selim Hemissi ◽  
Karim Saheb Ettabaa ◽  
Bassel Souleiman
Keyword(s):  
Hyperspectral Images ◽  
Multi Temporal

Compression of Hyperspectral Images by Using DWT and SPIHT

2017 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
VEERA NAVENDRA REDDY K ◽  
SIVAPPAGARI CHANDRA MOHAN REDDY ◽  
◽  
Keyword(s):  
Hyperspectral Images

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector
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