Low-Rank and Sparse Matrix Decomposition With Orthogonal Subspace Projection-Based Background Suppression for Hyperspectral Anomaly Detection

Yang, Yixin; Zhang, Jianqi; Song, Shangzhen; Zhang, Chi; Liu, Delian

doi:10.1109/lgrs.2019.2948675

Cited by 18 publications

(18 citation statements)

References 15 publications

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“…Using the low-rank and sparse matrices is to perform AD is not new and has been studied quite sometime in the past [20][21][22][23][24][25][26][27]. Its main idea is to take advantage of robust principal component analysis (RPCA) [46] or GoDec [48] or low rank and spare representation [25][26][27] to estimate BKG for suppression and a sparse component to characterize anomalies.…”

Section: Related Workmentioning

confidence: 99%

“…Following a similar idea to clustering weight proposed in [23], [24] also took advantage of the LRaSMD model to develop an OSP-based BKG suppression technique and further use RXD to estimate adaptive weights for anomalies. After OSP the interference of BKG in the sparse component can be suppressed effectively.…”

Section: Related Workmentioning

confidence: 99%

“…To address this dilemma several approaches introduced probabilities to weight potential anomalies in the sparse component to prevent them from being contaminated or influenced by BKG [23][24]. 3.…”

Section: Related Workmentioning

confidence: 99%

“…3. Finally, a third issue is how to design detection technique to extract anomalies after LRaSMD is used, for example, Euclidean distance [22], Mahalanobis distance [21], RXD in [24]. These three issues have direct impacts on the performance of LRaSMD.…”

Section: Related Workmentioning

confidence: 99%

“…For example, window-based local anomaly detectors, which implemented K or R using local windows [6][7], sliding windows [8], dual windows [9][10], multiple windows [11] and kernel anomaly detector [12], anomaly detection for unlabeled classification [13], real time processing of anomaly detection [14], guided filtering-based AD [15], spectral-spatial feature extraction-based AD [16], background separation-based AD [17], sparsity scoreestimation framework for AD [18]. Most recently, other approaches have been also developed such as deep learningbased anomaly detector, low rank and sparse matrix decomposition (LRaSMD) model-based anomaly detectors [19][20][21][22][23][24], low-rank and sparse representation [25][26][27], autoencoder [28][29][30], generative adversarial network (GAN) [31][32], game theory-based AD [33]. All of these works did not go beyond the original idea of RX/R-AD.…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection

Chang

Cao

Song

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Orthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due to its inherent requirement of prior target knowledge OSP has not been explored in anomaly detection. This paper takes advantage of an unsupervised OSP-based algorithm, automatic target generation process (ATGP) and a recently developed OSP-go decomposition (OSP-GoDec) along with data sphering (DS) to make OSP applicable to anomaly detection. Its idea is to implement ATGP on the background (BKG) and target subspaces constructed from the low-rank matrix L and sparse matrix S generated by OSP-GoDec to derive an OSP-based anomaly detector (OSP-AD). In particular, OSP-AD also includes DS to remove BKG interference from the target subspace so as to enhance anomaly detection. Surprisingly, operating data samples on different constructions of the BKG subspace and the target subspace yields various versions of OSP-AD. Experiments show that given an appropriate construction of the BKG subspace and the target subspace, OSP-AD can be shown to outperform existing anomaly detectors including Reed-Xiaoli anomaly detector and collaborative representation-based anomaly detector (CRD). Index Terms-Anomaly detection (AD), automatic target generation process (ATGP), data sphering (DS), go decomposition (GoDec), low rank and sparse matrix decomposition (LRaSMD), orthogonal subspace projection (OSP). OSP-based anomaly detector (OSP-AD).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection

Chang

Cao

Song

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation

Cheng,

Gao

2024

Sci Rep

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Anomaly detection (AD) has emerged as a prominent area of research in hyperspectral imagery (HSI) processing. Traditional algorithms, such as low-rank and sparse matrix decomposition (LRaSMD), often struggle to effectively address challenges related to background interference, anomaly targets, and noise. To overcome these limitations, we propose a novel method that leverages both spatial and spectral features in HSI. Initially, the original HSI is segmented into several subspaces using the k-means method, which reduces redundancy among HSI bands. Subsequently, the fractional Fourier transform (FrFT) is applied within each subspace, enhancing the distinction between background and anomaly target information while simultaneously suppressing noise. To further improve the stability and discriminative power of the HSI, LRaSMD is employed. Finally, the modified Reed–Xiaoli (RX) detector is utilized to identify anomalies within each subspace. The results from these detections are then aggregated to produce a comprehensive final outcome. Experiments conducted on five real HSI data sets yield an average area under the curve (AUC) of 0.9761 with a standard deviation of 0.0156 for the proposed algorithm. These results indicate that our method is highly competitive in the field of anomaly detection.

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A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

Zhao

Feng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Anomaly target detection methods for hyperspectral images (HSI) often have the problems of potential anomalies and noise contamination when representing background. Therefore, a spectral-spatial hyperspectral anomaly detection method is proposed in this article, which is based on fractional Fourier transform (FrFT) and saliency weighted collaborative representation. First, hyperspectral pixels are projected to the fractional Fourier domain by the FrFT, which can enhance the capability of the detector to suppress the noise and make anomalies to be more distinctive. Then, a saliency weighted matrix is designed as the regularization matrix referring to context-aware saliency theory and combined with the FrFT-based collaborative representation detector. The saliencyweighted regularization matrix assigns different pixels with different weights by using both spectral and spatial information, which can reduce the influence of the potential anomalous pixels embedded in the background when applying collaborative representation theory. Finally, to further improve the performance of the proposed method, a spectral-spatial detection procedure is employed to calculate final anomaly scores by using both spectral information and spatial information. The proposed method is compared with nine state-of-the-art hyperspectral anomaly detection methods on six HSI datasets, including two synthetic HSI datasets and four real-world HSI datasets. Extensive experimental results illustrate that the proposed method's detection performance outperforms other nine well-known compared methods in terms of area under the receiver operating characteristic (ROC) curve values, visual detection characteristics, ROC curve, and separability.

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Low-Rank and Sparse Matrix Decomposition With Orthogonal Subspace Projection-Based Background Suppression for Hyperspectral Anomaly Detection

Cited by 18 publications

References 15 publications

Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection

Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation

A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

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