Multiscale Superpixel Guided Discriminative Forest for Hyperspectral Anomaly Detection

Abstract

Recently, the isolation forest (IF) methods have received increasing attention for their promising performance in hyperspectral anomaly detection (HAD). However, limited by the ability of exploiting spatial-spectral information, existing IF-based methods suffer from a lot of false alarms and disappointing performance of detecting local anomalies. To overcome the two problems, a multiscale superpixel guided discriminative forest method is proposed for HAD. First, the multiscale superpixel segmentation is employed to generate some homogeneous regions, and it can effectively extract spatial information to guide anomaly detection for the discriminative forest in local areas. Then, a novel discriminative forest (DF) model with the gain split criterion is designed, which enhances the sensitivity of the DF to local anomalies by the utilization of multi-dimension spectral bands for node division; meanwhile, the acceptable range of hyperplane attribute values is introduced to capture any unseen anomaly pixels that are out-of-range in the evaluation stage. Finally, for the high false alarm rate situation in the existing IF-based algorithms, the multiscale fusion with guided filtering is put forward to refine the initial detection results from the DF. In addition, the extensive experimental results on four real hyperspectral datasets demonstrate the effectiveness of the proposed method.

Vector Decomposition-Based Arbitrary-Oriented Object Detection for Optical Remote Sensing Images

“…There are many types of remote-sensing-image-processing tasks, such as change detection [17][18][19], object detection [20][21][22], anomaly detection [23][24][25], etc. The objectdetection methods can be divided into bounding-box-based arbitrarily oriented object detection and horizontal-bounding-box-based general object detection.…”

Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary

“…Therefore, hyperspectral images (HSIs) [7] have been employed in various fields [8][9][10][11][12] in recent years. Hyperspectral technology contains many image processing tasks, such as change detection [13], classification [14,15], anomaly detection [16][17][18], fusion [5,19,20], band selection [21], and so on [22][23][24][25].…”

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

“…ECENTLY, the remote sensing image interpretation methods covering panchromatic image [1], [2], multispectral images [3], [4] and hyperspectral images (HSIs) [5] have achieved remarkable achievements. Among them, HSIs can provide rich spectral information (i.e., about 10-nm spectral resolution), which makes it possible to identify the ground objects with different characteristics by means of the spectral information [6]. Based on the above advantage, the HSIs are widely employed in the field of hyperspectral image classification [7]- [8], hyperspectral unmixing [9], [10], hyperspectral pansharpening [11], [12], band selection [13], [14], hyperspectral anomaly detection (HAD) [15], [16] and hyperspectral target detection [17], [18], etc.…”