Subspace analysis isolation forest for hyperspectral anomaly detection

doi:10.11947/j.AGCS.2021.20200036

Abstract

Abstract: Since the anomalies are usually “rare and different” in the hyperspectral image scene, they tend to be more easily isolated from the background pixels by appropriate splitting criterion. In view of this, we propose a hyperspectral anomaly detection method based isolation forest (iForest) with subspace analysis. Firstly, orthogonal subspace background suppression and dimension reduction techniques were used to improve the reliability of the isolation tree-splitting criterion. Secondly, the iForest-based detection may produce a number of false alarms since the forest is constructed using the randomly selected pixels in the whole scene. In order to solve this problem, the initial anomaly detection map was refined by local iForest processing. Compared with original iForest method, our approach can not only handle high dimensional problem, but also make full use of the local information. The experiments demonstrate the AUC score have been significantly improved in our approach.

Key words: hyperspectral imagery, anomaly detection, isolation forest, orthogonal subspace, principal component analysis

CLC Number:

P237

HUANG Yuancheng, XUE Yuanyuan, LI Pengfei. Subspace analysis isolation forest for hyperspectral anomaly detection[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 416-425.

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