Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection
文献类型:期刊论文
作者 | Wang, Nan2,3; Shi, Yuetian2,3; Li, Haiwei1,3; Zhang, Geng1,3; Li, Siyuan1,3; Liu, Xuebin1,3 |
刊名 | REMOTE SENSING |
出版日期 | 2023-09 |
卷号 | 15期号:18 |
ISSN号 | 2072-4292 |
关键词 | hyperspectral anomaly detection deep learning band selection autoencoder |
DOI | 10.3390/rs15184430 |
产权排序 | 1 |
英文摘要 | Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the curse of dimensionality, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes l2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors. |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:001074418700001 |
源URL | [http://ir.opt.ac.cn/handle/181661/96827] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Zhang, Geng |
作者单位 | 1.Shaanxi Key Lab Opt Remote Sensing & Intelligent I, Xian 710100, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710100, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Nan,Shi, Yuetian,Li, Haiwei,et al. Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection[J]. REMOTE SENSING,2023,15(18). |
APA | Wang, Nan,Shi, Yuetian,Li, Haiwei,Zhang, Geng,Li, Siyuan,&Liu, Xuebin.(2023).Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection.REMOTE SENSING,15(18). |
MLA | Wang, Nan,et al."Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection".REMOTE SENSING 15.18(2023). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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