SONG et al.: SSIIFD FOR HYPERSPECTRAL ANOMALY DETECTIO 1  Abstract—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. Index Terms—Hyperspectral image (HSI), anomaly detection, isolation forest, spectral-spatial information. I. INTRODUCTION YPERSPECTRAL image (HSI) with hundreds of contiguous bands for each pixel can provide abundant spectral and spatial information simultaneously [1]. HSI has been widely applied in many remote sensing applications, such as anomaly detection [2], [3], classification [4], and change detection [5]. Among these applications, hyperspectral anomaly detection has received extensive attention. A wide variety of methods have been developed, which aims at distinguishing Manuscript received April 15, 2021. This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61801455). (corresponding author: Bin He.) Xiangyu Song is with the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China, and also with the University of Chinese Academy of Sciences, Beijing 100049, China. (e-mail: songxiangyu17@ mails.ucas.edu.cn). Sunil Aryal is with the School of Information Technology, Deakin University, 75 Pigdons Rd, Waurn Ponds, VIC, 3216, Australia. (e-mail: sunil.aryal@deakin.edu.au). outliers, whose spectral and spatial signatures are highly distinct from their surrounding pixels or the global background in an unsupervised way. In the literature, most methods have concentrated on examination of the role of HSI spectral signatures in anomaly detection, employing exclusively the spectrum of a given pixel to determine its outlier status. The statistical model-based technique is the first category in hyperspectral anomaly detection. One of the most well-known methods is the Reed- Xiaoli (RX) algorithm [6], proposed by Irving S. Reed and Xiaoli Yu, which is considered as the main benchmark method. The RX detector assumes that the background can be modeled by employing multivariate Gaussian distributions. The RX detector has two versions, i.e., the global RX and local RX (LRXD), where LRXD models the background with neighborhood pixels. However, most real-world hyperspectral images (HSIs) cover different classes of materials and exhibit complex backgrounds, which means that the Gaussian distribution assumption is oversimplified in real-world HSIs. Therefore, several variants of the RX detector have been proposed [7]-[12]. For example, the kernel RX [7] detector is a nonlinear version of the RXD, which calculates the Mahalanobis distance between the pixels to be tested and the background in higher dimensional feature space with the kernel theory. The cluster-based anomaly detector (CBAD) [8] segments the whole HSI into several clusters and then detects anomalies in each cluster with the RX detector. Zhou et al. proposed a novel cluster kernel RX detector [12] to accelerate the kernel RX detector by partitioning the whole HSI into several clusters and then employing a fast eigenvalue decomposition algorithm to obtain detection results. In addition to statistical model-based methods, there are many other types of detectors. For example, the orthogonal subspace projection (OSP) is a typical geometrical modeling- based method. Chang et al. [54], [55] developed OSP-based Kai Ming Ting is with National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China. (e-mail: tingkm@nju.edu.cn). Zhen Liu is with both Big Data Research Center and Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. (e-mail: quake@uestc.edu.cn). Bin He is with the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China. (e-mail: hebin@ciomp.ac.cn). Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest Xiangyu Song, Sunil Aryal, Member, IEEE, Kai Ming Ting, Zhen Liu, and Bin He H