Anomaly detection in hyperspectral image (HSI) 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 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. Furthermore, 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 datasets demonstrate that the proposed detector outperforms other state-of-the-art methods.

Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.

This letter presents an innovative application of the isolation using nearest neighbor ensemble (iNNE) method for hyperspectral anomaly detection. iNNE is an efficient anomaly detector based on nearest neighbors and isolation. Based on iNNE, we propose a novel isolation-based hyperspectral anomaly detection framework that detects anomalies as follows. First, the Gabor filter is applied to extract spatial information from the PCA-projected subspace. Gabor features are then employed as the input to the ReMass-iForest to obtain the spatial anomaly score. Then the iNNE is employed to derive the spectral anomaly score. Finally, we integrate the detection outcomes by linearly combining the acquired spatial and spectral anomaly scores to predict anomaly pixels. Compared with the existing tree-based isolation method, such as the iForest, the proposed iNNE-based method addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Experimental analysis across five hyperspectral datasets indicates that the proposed detector consistently attains the highest AUC scores and exhibits improved ROC curves in comparison to established benchmarks.

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.