Robust anomaly detection algorithm for hyperspectral images using spectral unmixing

2021 ◽  
Author(s):  
Ahmed Elrewainy ◽  
Sherif Sherif
Keyword(s):  
Anomaly Detection ◽  
Detection Algorithm ◽  
Spectral Unmixing ◽  
Hyperspectral Images

scholarly journals Joint Anomaly Detection and Spectral Unmixing for Planetary Hyperspectral Images

2016 ◽  
Vol 54 (12) ◽  
pp. 6879-6894 ◽  
Author(s):  
Sina Nakhostin ◽  
Harold Clenet ◽  
Thomas Corpetti ◽  
Nicolas Courty
Keyword(s):  
Anomaly Detection ◽  
Spectral Unmixing ◽  
Hyperspectral Images

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.

scholarly journals Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

2021 ◽  
Vol 13 (20) ◽  
pp. 4102
Author(s):  
Genping Zhao ◽  
Fei Li ◽  
Xiuwei Zhang ◽  
Kati Laakso ◽  
Jonathan Cheung-Wai Chan
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Spectral Data ◽  
Target Detection ◽  
Reconstruction Error ◽  
Spectral Unmixing ◽  
Hyperspectral Images ◽  
Background Signal ◽  
Archetypal Analysis ◽  
Target Signal

Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

scholarly journals Correction: Zhang, G., et al. Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing. Remote Sensing 2020, 12, 3985

2021 ◽  
Vol 13 (3) ◽  
pp. 473
Author(s):  
Guichen Zhang ◽  
Daniele Cerra ◽  
Rupert Müller
Keyword(s):  
Remote Sensing ◽  
Spectral Unmixing ◽  
Shadow Detection ◽  
Hyperspectral Images

The authors would like to make the following correction of [...]

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