کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8145636 | 1524094 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
فیزیک اتمی و مولکولی و اپتیک
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چکیده انگلیسی
Nowadays, some algorithms based on deep learning have drawn increasing attention in hyperspectral image (HSI) analysis. In this paper, we propose spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition (LRaSMD-SSSAE) for hyperspectral anomaly detection (AD). First, the Go Decomposition (GoDec) algorithm is employed to solve the low-rank background component and the sparse anomaly component. Second, stacked autoencoders (SAE) are employed on the sparse matrix for spectral deep features and on the low-rank matrix for spatial deep features, respectively. Finally, the spectral-spatial feature matrix is established and local Mahalanobis-distance algorithm is employed for the final detection result. Experiments are carried out on real and synthetic HSI, and the results show that the proposed LRaSMD-SSSAE generally outperforms the comparison algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Infrared Physics & Technology - Volume 92, August 2018, Pages 166-176
Journal: Infrared Physics & Technology - Volume 92, August 2018, Pages 166-176
نویسندگان
Chunhui Zhao, Lili Zhang,