Background Whitened Target Detection Algorithm for hyperspectral imagery

Hyperspectral remotely sensed imagery is rapidly developed recently. It collects radiance from the ground with hundreds of channels which results in hundreds of co-registered images. How to process this huge amount of data is a great challenge, especially when no information of the image scene is available. Under this circumstance, anomaly detection becomes more difficult. Several methods are devoted to this problem, such as the well-known RX algorithm and high-moment statistics approaches. The RX algorithm can detect all anomalies in single image but it can not discriminate them. On the other hand, the high-moment statistics approaches use criterion such as skewness and kurtosis to find the projection directions to detect anomalies. In this paper we propose an effective algorithm for anomaly detection and discrimination extended from RX algorithm, called Background Whitened Target Detection Algorithm. It first modeled the background signature with Gaussian distribution and applied the whitening process. After the process, the background will distribute as i.i.d. Gaussian in all spectral bands. Those pixels did not fit in the distribution will be the anomalies. Then Automatic Target Detection and Classification Algorithm (ATDCA) is applied to search for those distinct spectrum automatically and classify them as anomalies. Since ATDCA can also estimated the abundance fraction of each target resident in one pixel by applying Sum-to-one and Nonnegativity constraints, the proposed method can also be applied in a constrained fashion. The experimental results show that the proposed method can improve RX algorithm by discriminate the anomalies and also outperform high-moment approaches in terms of computational complexity.