Anomaly
detection in hyperspectral image is affected by redundant bands and the limited
utilization capacity of spectral-spatial information. In this article, we
propose a novel Improved Isolation Forest (IIF) algorithm based on the
assumption that anomaly pixels are more susceptible to isolation than the
background pixels. The proposed IIF is a modified version of the Isolation Forest
(iForest) algorithm, which addresses the poor performance of iForest in detecting
local anomalies and anomaly detection in high-dimensional data. Further, we
propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full
use of global and local information, as well as spectral and spatial
information. To be specific, first, we apply the Gabor filter to extract
spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain
the spatial anomaly score. Next, original images are divided into several
homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the
preprocessed images are then employed as input to the proposed IIF detector to
obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by
combining them linearly to predict anomaly pixels. The experimental results on four real
hyperspectral data sets demonstrate that the proposed detector outperforms
other state-of-the-art methods.