Abstract
Hyperspectral anomaly detection has drawn much attention in recent years. In this paper, in order to effectively extract anomalies in hyperspectral images, a novel sparse-representation based hyperspectral anomaly detection method via adaptive background sub-dictionaries is proposed. Firstly, a background estimation strategy is proposed to provide representative background information. Based on the estimated background, a global dictionary is constructed by utilizing K-means clustering algorithm. Next, Several active atoms are selected from the global dictionary to form a sub-dictionary to adaptively approximate the local region in each dual-window. This sub-dictionary construction strategy can remove potential anomaly contamination in local regions. Finally, a re-weighting strategy is proposed to enhance the performance of sparse-representation-based anomaly detector. Experimental results demonstrate that our method can effectively extract anomalies and suppress background simultaneously.
Highlights
Hyperspectral images (HSIs) are capable to contain abundant spectral characteristics of ground materials [1]
In this paper, inspired by the work of Zhu et al [14], and from the perspective of dictionary construction for Sparse representation (SR), we propose a novel hyperspectral anomaly detection method based on adaptive background sub-dictionaries
We propose an anomaly detection method based on sparse representation via adaptive background subdictionaries
Summary
Hyperspectral images (HSIs) are capable to contain abundant spectral characteristics of ground materials [1]. Huang: Sparse Representation Based Hyperspectral Anomaly Detection via Adaptive Background Sub-Dictionaries anomaly pixels from the background, the kernel RX algorithm [8] projects the HSI dataset into a higher dimensional feature space. In LRR model, HSI data is assumed drawn from multiple subspaces Based on this assumption, the background part and the anomaly part are able to be separated by a background dictionary. In this paper, inspired by the work of Zhu et al [14], and from the perspective of dictionary construction for SR, we propose a novel hyperspectral anomaly detection method based on adaptive background sub-dictionaries. The basic idea of SR based anomaly detection is to represent the test pixel with the linear combination of the background dictionary atoms. Ri is the reconstruction residual of the pixel xi. if the residual ri is larger than a given threshold, the test pixel xi is considered to be an anomalous pixel
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