Anomaly detection algorithms are based on the assumption that the background follows a multivariate normal distribution. An anomaly emerges when a deviation occurs in the distribution of the background model. Another assumption is that the spectral features of the background and the anomaly are uncorrelated. However

clutter background often has spectral features that are too complex to be accurately described by a background model. In fact

the complex spectral features of different ground objects are correlated with the features of anomaly targets to some extent. Hence

background information may be contaminated by other anomalous and noisy signals. Moreover

the false alarm rate of detection results increases because noise or background pixels may be wrongly detected as anomalies. In other words

the anomaly detection method based on hypothesis testing is sensitive to anomalies.Two main factors explain high false alarm rates in detection results. One factor is the limited capability of models to characterize backgrounds. An effective background model must reduce the correlation between backgrounds and anomalies. The other factor is the judgment of uncertain areas

which differ from backgrounds but are not sufficiently anomalous to be marked as anomalies.Inspired by the biological theory of fly vision

we proposed an anomaly detection algorithm for spectral anomaly targets in remote sensing images. A parallel multi-aperture model was constructed to adaptively and separately model the spectral features of multiple kinds of background objects. Depending on the significance of an anomaly

the anomaly

background

and uncertain area were marked by the relative Mahalanobis distance. Hence

the proposed algorithm can reduce the influence of correlation between anomalies and backgrounds and remedy the disadvantages of traditional hypothesis testing methods

which cannot distinguish uncertain areas and the absence of anomalies. The detection results from the multi-aperture model were then fused to obtain the anomaly detection results. The simulated experiment focused on the anomaly detection of clutter areas containing various background objects.Both synthetic data and real data were applied to verify the effectiveness of the proposed method. Experiment results show that the proposed algorithm achieves better performance compared with other classic algorithms. Under clutter background

the proposed method can still detect anomalies

as well as the shape and size of a certain target. The proposed algorithm only marks the most significantly abnormal areas as targets. An uncertain area detected by one aperture is estimated again through fusion.The multi-aperture model reduced the correlation of spectral features between the background and anomaly to some extent. The background-sensitive algorithm can distinguish uncertain areas and backgrounds and precisely detect the shape and size of targets

especially those with distributed densities. A soft judgment is deemed robust to background models; hence

the proposed algorithm is robust to clutter backgrounds.