15 September 2022 Collaborative representation with multipurification processing and local salient weight for hyperspectral anomaly detection
Nan Wang, Yuetian Shi, Fanchao Yang, Geng Zhang, Siyuan Li, Xuebin Liu
Author Affiliations +
Abstract

Anomalous objects detection for hyperspectral imagery is a significant branch in the area of remote sensing. Although enormous advancements have been developed, issues of redundancy of spectral information and correlation between pixels should be further explored and improved. To address these problems, we proposed a method that is on the basis of integrating collaborative representation with multipurification processing and local salient weight. Multipurification processing consists of spectral bands purification (SBP) and background purification (BGP). First, to alleviate the interference of redundant spectral information, we remove unnecessary spectral bands by adopting SBP based on considering the global spectral intensity of each band. Then, we remove the outliers in the local dual window by BGP to avoid the effect of heterogeneous pixels. Simultaneously, we obtain the local salient weight by calculating the similarity and difference of pixels in the dual window. Next, we obtain the initial detection result by a collaborative representation, which has been testified to be very effective. Finally, combined with the local salient weight map, the initial detection map is improved to the final detection map. To demonstrate the superiority of the proposed method, we conducted the comprehensive experiment on three public benchmark datasets that contain 15 hyperspectral images.

© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
Nan Wang, Yuetian Shi, Fanchao Yang, Geng Zhang, Siyuan Li, and Xuebin Liu "Collaborative representation with multipurification processing and local salient weight for hyperspectral anomaly detection," Journal of Applied Remote Sensing 16(3), 036517 (15 September 2022). https://doi.org/10.1117/1.JRS.16.036517
Received: 22 March 2022; Accepted: 15 August 2022; Published: 15 September 2022
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Sensors

Hyperspectral imaging

Visualization

Databases

Remote sensing

Statistical analysis

Target detection

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