Anomaly detector has become increasingly important in remote sensing data analysis and has been used in many applications

such as environmental and agricultural monitoring

geological exploration

and national defense security. According to special spectral content

an anomaly target has an obvious edge feature

which corresponds to a high frequency. By contrast

the background corresponds to a low frequency because of its smooth spectral content. Considering different spectral contents from the background

the anomaly target can be filtered out from the high frequency of the edge. A fast anomaly detector has been proposed to detect anomaly by linear filter of the spatial domain. However

texture and detail of clutter background also have the characteristic of high frequency. Linear filter has difficulty separating the anomaly from the clutter background accurately. Compared with bright background object

spatial salience of anomaly will be decreased. Furthermore

small size of anomaly will lead to subpixel anomaly

which will blur the edge feature of the target. A small anomaly target may not be successfully detected by a spatial filter. Conversely

cross analysis of a binary image reduced the complexity of computation. However

self-correlation of the large anomaly target will lead to a hollow effect in the center area. Inspired by the nonlinear filter mechanism of biotical vision

a bionic anomaly detection algorithm is proposed. In the natural world

a biotical vision system can accurately detect a small moving target

even in a cluttered environment. Redundancy information of the background will be inhibited because of its invariance on the spatial or temporal domain. Only features can be maintained as a high-order feature caused by a variance on the spatial and temporal domains. In fact

an anomalous spectral content of the target not only reflects a single band (spatial domain) but also reflects all the bands. Inspired by biotical vision

a correlated-type nonlinear filter is proposed to extract the high-order feature within the joint spatial and spectral domain. Like a moving target

the anomaly can be detected because of its spatial spectral wave

which contains spectral content of all bands. Simultaneously

the clutter background will be inhibited effectively because of its correlation with the spectral wave within the local spatial domain. Furthermore

the inner window is applied as a protective band

which can prevent the correlation of the anomaly target self

to avoid the hollow effect of a large anomaly target. Simulated and real data were applied to verify the utility of the proposed method. Experimental results show that the proposed anomaly detector has a good performance for small anomalies

which are rounded by clutter background. For a larger anomaly target

the hollow effect had to be removed within cross analysis by the protective band. This study proposed a bionic anomaly detector based on nonlinear filter. The high-order feature is extracted by nonlinear filter

with joint spatial and spectral information. The high-order feature has a strong robustness under the clutter background

particularly for a small target. Simultaneously

the inner window as protective band improves the hollow effect of a large anomaly target.