Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery

Rong Wang, Feiping Nie, Zhen Wang, Fang He, Xuelong Li

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Hyperspectral anomaly detection (HAD) has drawn a significant attention of late due to its importance in many military and civilian applications. In this article, a fast hyperspectral anomaly detector that combines multiple features and isolation forest is proposed. This approach, which is based on the assumption that the anomalous pixels are more susceptible to isolation than the background pixels, consists of two main parts. First, the spectral, Gabor, extended morphological profile (EMP) and extended multiattribute profile (EMAP) features are extracted from the hyperspectral image (HSI). Next, the isolation forest of each feature is constructed using the subsampling strategy. This combination of multiple features can exploit both the spectral and spatial information of the HSI, thereby improving the anomaly detection performance significantly. Compared with eight state-of-the-art HAD methods, the experimental results on four real hyperspectral data sets demonstrate that the performance of our proposed approach is quite competitive in terms of detection accuracy and running time.

Original languageEnglish
Article number9040873
Pages (from-to)6664-6676
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume58
Issue number9
DOIs
StatePublished - Sep 2020

Keywords

  • Hyperspectral anomaly detection (HAD)
  • hyperspectral image (HSI)
  • isolation forest
  • multiple features

Fingerprint

Dive into the research topics of 'Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery'. Together they form a unique fingerprint.

Cite this