Abstract
In hyperspectral anomaly detection, the dual-window-based detector is a widely used technique that employs two windows to capture nonstationary statistics of anomalies and back- ground. However, its detection performance is usually sensitive to the choice of window sizes and suffers from inappropriate window settings. In this work, a decision-fusion approach is pro- posed to alleviate such sensitivity by merging the results from multiple detectors with different window sizes. The proposed approach is compared with the classic Reed-Xiaoli (RX) algorithm as well as kernel RX (KRX) using two real hyperspectral data. Experimental results demonstrate that it outperforms the existing detectors, such as RX, KRX, and multiple-window-based RX. The overall detection framework is suitable for parallel computing, which can greatly reduce computational time when processing large-scale remote sensing image data. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or repro- duction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.9.097297) tional probability density functions under the two hypotheses (without and with anomaly) are assumed to be Gaussian. The solution turns out to be an adaptive Mahalanobis distance between the pixel under test and the local background. It is preferred to use local background to capture nonstationary statistics, and its advantage of using a global background covariance matrix has been demonstrated in the literature. 11-13 The RX detector has become the benchmark of anomaly detection algorithms in HSI. Obviously, the key to success is an appropriate estimate of a local background covariance matrix for effective background suppression. An adaptive RX detector employs a dual-window strategy: the inner window is slightly larger than the pixel size, the outer window is even larger than the inner one, and only the samples in the outer region (i.e., between the frames of inner and outer windows) are used to estimate the background covariance matrix to avoid the use of the potential anomalous pixels. Intuitively, the number of pixels in the outer region (related to the sizes of inner and outer windows) should be more than the number of bands so that the resulting covariance matrix can be full-rank for inverse matrix operation. However, even when the covariance matrix is ill-rank, its inversion can still be computed by several strategies, such
Highlights
We propose a decision-fusion approach for hyperspectral anomaly detection using multiple windows, where a decision map is produced for each dual-window detector and the final decision map is generated with a voting strategy
We proposed an effective decision-fusion strategy for dual-window-based anomaly detection in Hyperspectral imagery (HSI)
Experimental results of two hyperspectral data demonstrated that the proposed RX-Fusion/ kernel RX (KRX)-Fusion outperformed the existing RX, KRX, multiple-window-based RX (MW-RX), and MW-KRX
Summary
Hyperspectral imagery (HSI) contains hundreds of contiguous spectral bands which enable the discrimination of different materials and make a variety of potential civilian and military applications possible.[1,2] Target detection is the ability to detect a low-probability target with a known signature from an unknown background.[3,4,5] When the target spectral signature is unknown, unsupervised anomaly detection has to be applied, which is a method used to find anomalous pixels whose spectral signatures are different from their surroundings.[6,7] As a classic anomaly detector, the Reed-Xiaoli (RX) algorithm[8,9,10] was developed under a hypothesis testing where the conditional probability density functions under the two hypotheses (without and with anomaly) are assumed to be Gaussian. An adaptive RX detector employs a dual-window strategy: the inner window is slightly larger than the pixel size, the outer window is even larger than the inner one, and only the samples in the outer region (i.e., between the frames of inner and outer windows) are used to estimate the background covariance matrix to avoid the use of the potential anomalous pixels. Different background modeling approaches were proposed, such as support vector data description,[24] automated modeling methods in Ref. 25, and the collaborative-representation-based method.[26] the dual-window-based RX algorithm remains the benchmark due to its relative robustness and easy implementation. We propose a decision-fusion approach for hyperspectral anomaly detection using multiple windows, where a decision map is produced for each dual-window detector and the final decision map is generated with a voting strategy. Experimental results will demonstrate that the proposed strategy can reduce the false alarm rates when maintaining the same true positive rates
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