IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018
DOI: 10.1109/igarss.2018.8517395
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A Distributed and Parallel Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

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Cited by 3 publications
(4 citation statements)
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“…There are also detection algorithms based on sparse representation [15,16], which represent the image background as a more representative basis vector or spectrum and use the product of spectral prior knowledge and related parameters to represent the original hyperspectral data. Li et al proposed the BJSR (background joint sparse representation) algorithm [17], an anomaly detection algorithm for hyperspectral images using background joint sparse representation by estimating an adaptive orthogonal background complementary subspace by BJSR, which adaptively selects the most representative background basis vectors for local regions, and then proposed an unsupervised adaptive subspace detection method to suppress the background and highlight the anomalous components at the same time.…”
Section: Related Wordmentioning
confidence: 99%
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“…There are also detection algorithms based on sparse representation [15,16], which represent the image background as a more representative basis vector or spectrum and use the product of spectral prior knowledge and related parameters to represent the original hyperspectral data. Li et al proposed the BJSR (background joint sparse representation) algorithm [17], an anomaly detection algorithm for hyperspectral images using background joint sparse representation by estimating an adaptive orthogonal background complementary subspace by BJSR, which adaptively selects the most representative background basis vectors for local regions, and then proposed an unsupervised adaptive subspace detection method to suppress the background and highlight the anomalous components at the same time.…”
Section: Related Wordmentioning
confidence: 99%
“…Here, ClassNum is the number of models that can be classified, and 3 is the three prediction frames that the model predicts for each pixel. The target coordinates lose the predicted output sizes for (52, 52, 15), (26,26,15) and (13,13,15). 15 is calculated from the coordinate point and confidence from the three boxes, whereas the confidence level is used to determine if there is a target in the box.…”
Section: Sehyp Headmentioning
confidence: 99%
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“…Ren capitalized on the characteristic that the high-order automatic anomaly detection algorithm may descend into a local extreme point after randomly selecting the initial vector, facilitating parallel detection under diverse situations [30]. Liu optimized the target detection algorithm based on low-rank sparse representation [31]. They first segmented the image using the narrow dependency of hyperspectral data on the high-performance computing platform Spark, then used parallel clustering algorithm to cluster the pixels of hyperspectral images, and finally computed the clustered data in parallel, significantly improving the speed and scalability of the method.…”
Section: Introductionmentioning
confidence: 99%