Sequential Band Fusion for Hyperspectral Anomaly Detection

被引：6

作者：

Song, Meiping ^{[1
]}

Li, Fang ^{[1
]}

Yu, Chunyan ^{[1
]}

Chang, Chein-I ^{[2
,3
]}

机构：

[1] Dalian Maritime Univ, Informat & Technol Coll, Ctr Hyperspectral Imaging Remote Sensing CHIRS, Dalian 116026, Peoples R China

[2] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA

[3] Providence Univ, Dept Comp Sci & Informat Management, Taichung 02912, Taiwan

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2022年 / 60卷

关键词：

Hyperspectral imaging; Detectors; Correlation; Anomaly detection; Fuses; Sparse matrices; Indexes; band fusion (BF); band sequential (BSQ); sequential band fusion (SBF); DIMENSIONALITY REDUCTION; RX-ALGORITHM; SELECTION; CLASSIFICATION; SEPARATION; PROJECTION; SUBSET;

D O I：

10.1109/TGRS.2021.3069734

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

This article proposes a new approach to hyperspectral band processing for anomaly detection, to be called sequential band fusion (SBF), derived from the band sequential (BSQ) data acquisition format used by a hyperspectral imaging sensor which fuses one single band at a time with previously fused band subset sequentially. In order to realize SBF, four versions, SBF-BSQ, initial band driven SBF (IBD-SBF), band prioritization SBF (BP-SBF), and band selection SBF (BS-SBF), are developed. Furthermore, to validate the sequentially fused results by SBF identical to that produced by combining all joint bands together, its mathematical theory and derivations are also presented. Finally, the full utility of SBF in anomaly detection is demonstrated through extensive experiments.

引用

页数：16

共 65 条

[1]

[Anonymous], 2003, Hyperspectral imaging: techniques for spectral detection and classification

[2]

Borghesi A, 2019, AAAI CONF ARTIF INTE, P9428

[3] Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity [J].