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Hyperspectral Anomaly Detection Using Combined Similarity Criteria

Anomaly detection is one of the practical applications in hyperspectral imagery (HSI) over the last two decades. In this paper, we propose a combined similarity criterion anomaly detector (CSCAD) method for HSI anomaly detection. The proposed method approximates the background using the surrounding neighbor pixels. Then, the residual error is obtained by subtracting the approximated background from the original data. Similarity criterion between the central and the adjacent pixels is applied simultaneously to detect anomalies more precisely. Finally, the criterion number is calculated for each pair of the central-adjacent pixels in the dual window, and then anomalies are detected. The main contribution of the paper is removing the noise and outlier, employing an efficient method that conducts us to more accurate detection performance. Contamination of the noise and outlier is reduced by the residual error and similarity criterion values. So, applying the criterion numbers is an accurate method for removing the outliers and noise without any need to use an additive stage that was introduced in our previous work. We implement the proposed method on three real hyperspectral images. CSCAD outputs are depicted by receiver operating characteristic curves, area under curve values, and intuitive images. Comparing the results of the proposed method with eight popular and the state-of-the-art methods proves that the CSCAD is an accurate and effective method to detect anomalies.