A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile

Liu, Senhao; Zhang, Lifu; Yi, Chuixiang; Chen, Likun; Wang, Yibo

doi:10.3390/rs13193954

Cited by 2 publications

(3 citation statements)

References 42 publications

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“…The size of window: (3, 5), (3,5), (13,31), (5, 7) The outputting sensitivity of spatial distance weight: 0.8, 5, 10, 0.5 The outputting sensitivity of spectral distance weight: 0.3, 1, 1, 0.3…”

Section: Bfadmentioning

confidence: 99%

“…The size of window: (3, 5), (5, 7), (13,21), (3,5) The fractional order: 0.8, 0.5, 0.8, 0.9 The Lagrange multiplier: 0.5, 1, 0.1, 0.1 The weighting coefficient: 0.5, 0.1, 0.1, 0.1…”

Section: Ssfscrdmentioning

confidence: 99%

“…In the hyperspectral imaging process, the spectral information and spatial information of the ground targets can be obtained at the same time. The obtained tens or hundreds of spectral bands can be regarded as approximately continuous [1][2][3][4][5]. Hence, the obtained hyperspectral image (HSI) can more comprehensively reflect the subtle differences among different ground targets that cannot be detected by broadband remote sensing [6].…”

Section: Introductionmentioning

confidence: 99%

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Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Xiang

Song

et al. 2022

Remote Sensing

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Hyperspectral anomaly detection has become an important branch of remote–sensing image processing due to its important theoretical value and wide practical application prospects. However, some anomaly detection methods mainly exploit the spectral feature and do not make full use of spatial features, thus limiting the performance improvement of anomaly detection methods. Here, a novel hyperspectral anomaly detection method, called spectral–spatial complementary decision fusion, is proposed, which combines the spectral and spatial features of a hyperspectral image (HSI). In the spectral dimension, the three–dimensional Hessian matrix was first utilized to obtain three–directional feature images, in which the background pixels of the HSI were suppressed. Then, to more accurately separate the sparse matrix containing the anomaly targets in the three–directional feature images, low–rank and sparse matrix decomposition (LRSMD) with truncated nuclear norm (TNN) was adopted to obtain the sparse matrix. After that, the rough detection map was obtained from the sparse matrix through finding the Mahalanobis distance. In the spatial dimension, two–dimensional attribute filtering was employed to extract the spatial feature of HSI with a smooth background. The spatial weight image was subsequently obtained by fusing the spatial feature image. Finally, to combine the complementary advantages of each dimension, the final detection result was obtained by fusing all rough detection maps and the spatial weighting map. In the experiments, one synthetic dataset and three real–world datasets were used. The visual detection results, the three–dimensional receiver operating characteristic (3D ROC) curve, the corresponding two–dimensional ROC (2D ROC) curves, and the area under the 2D ROC curve (AUC) were utilized as evaluation indicators. Compared with nine state–of–the–art alternative methods, the experimental results demonstrate that the proposed method can achieve effective and excellent anomaly detection results.

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Section: Bfadmentioning

confidence: 99%

“…The size of window: (3, 5), (5, 7), (13,21), (3,5) The fractional order: 0.8, 0.5, 0.8, 0.9 The Lagrange multiplier: 0.5, 1, 0.1, 0.1 The weighting coefficient: 0.5, 0.1, 0.1, 0.1…”

Section: Ssfscrdmentioning

confidence: 99%