Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation

Abstract

Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others.

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection

“…Verification experiments on two real typical hyperspectral datasets were conducted in this section to demonstrate the anomaly detection performance of the proposed spectrabased selective searching (Triple-S) algorithm. Five previous excellent anomaly detection methods, such as GRX [19], FEE [29], FEBPAD [30], LSAD-CR-IDW [40] and LRSRD [38] were used to verify the effectiveness of the Triple-S algorithm proposed in this paper. The validity of the proposed Triple-S method and these representative methods for comparison on these two real hyperspectral datasets are displayed, including detection results, receiver operating characteristic (ROC) curve [47], the area under the ROC curve (AUC) values [48] and the running time.…”

“…To assess our Triple-S anomaly detection algorithm both qualitatively and quantitatively, an experiment was conducted to evaluate the anomaly detection performance of the proposed Triple-S algorithm. Meanwhile, five typical anomaly detection algorithms, GRX [19], FEE [29], FEBPAD [30], LSAD-CR-IDW [40] and LRSRD [38], were employed for comparison and the corresponding detection results were obtained from the source code shared on GitHub.…”

“…In Ref. [40], the various local spatial distribution information of the neighboring pixels of a test pixel were considered by adding a summation strategy to the local window in the CRD algorithm, which improved the accuracy of linear representation and the detection performance, but this came at the cost of increased computational complexity and time, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW).…”

A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

“…There is also a possibility that the anomalous pixels are inside the neighborhoods, which will decrease the performance of CRD. In order to address this issue, variants of CRD were proposed [26][27][28]. A recent algorithm, called spatial density background purification (SDBP) [29], also aimed to solve this problem.…”

Hyperspectral Anomaly Detection via Spatial Density Background Purification

“…The algorithm is directly based on the fact that each pixel in the background can be approximated by its spatial neighborhood, while abnormal pixels cannot. Considering the spatial information in adjacent pixels, Tan et al proposes two improved methods based on local summation anomaly detection (LSAD) [45]. Firstly, a partial summation unsupervised recently regularized subspace with outlier anomaly detector is proposed.…”