Related papers: Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery

Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery

URL: http://arxiv.org/abs/2101.01976v1
Date: Wed, 6 Jan 2021 11:23:51 GMT
Title: Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
Authors: Rong Wang, Wei Feng, Qianrong Zhang, Feiping Nie, Zhen Wang, and Xuelong Li
Abstract summary: We propose a novel ensemble and random collaborative representation-based detector (ERCRD) for hyperspectral anomaly detection (HAD) Our experiments on four real hyperspectral datasets exhibit the accuracy and efficiency of this proposed ERCRD method compared with ten state-of-the-art HAD methods.
Score: 133.83048723991462
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields. As a classic HAD method, the collaboration representation-based detector (CRD) has attracted extensive attention and in-depth research. Despite the good performance of CRD method, its computational cost is too high for the widely demanded real-time applications. To alleviate this problem, a novel ensemble and random collaborative representation-based detector (ERCRD) is proposed for HAD. This approach comprises two main steps. Firstly, we propose a random background modeling to replace the sliding dual window strategy used in the original CRD method. Secondly, we can obtain multiple detection results through multiple random background modeling, and these results are further refined to final detection result through ensemble learning. Experiments on four real hyperspectral datasets exhibit the accuracy and efficiency of this proposed ERCRD method compared with ten state-of-the-art HAD methods.

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