Multi-aperture anomaly detector for clutter background

Without priori information, anomaly detector has more important utility compared with supervised target detection. Many classical anomaly detectors have obtained perfect performance in many situations. However, there still have two problems which are correlated with accuracy of anomaly detector. Firstly, clutter background induced more and more difficult pixel which have moderate statistical difference. Then, ideal uncontaminated subset of clutter background is hard to be obtain which is used to estimate background model. Secondly, difference of spectral content of different background objects will effect salience of anomaly targets. And it is arbitrary that uncertain pixels is nominated as non-anomalies by one threshold. For above two problems, a multi-aperture anomaly detector is proposed in this paper. Without selection of anomaly-free pixels and accurate statistical model, the proposed anomaly detector is expected to decrease false alarm rate with clutter background. A multi-aperture division for hyperspectral cube is conducted by iterative process. Statistical data of ever subaperture will be named as basis, which represent spectral characteristic of a certain range of spectral cube. Then, anomaly salience is proposed to measure the difference between pixels and sub-aperture basis. On the other hand, continuity of membership value based on fuzzy logical theory is more suitable to nominate difficulty pixels which has moderate anomaly salience. At last defuzzification ruler can be used to fuse different detection results from multi-aperture.