遥感异常目标的仿生非线性滤波检测

目的 为了解决复杂背景干扰下基于线性滤波异常检测算法无法有效区分复杂背景特征与异常目标特征，导致检测结果虚警率偏高的问题，提出一种面向复杂背景的遥感异常小目标仿生非线性滤波检测算法。方法 受生物视觉系统利用不同属性信息挖掘高维特征机理的启发，该算法通过相关型非线性滤波器综合多波段光谱数据提取高维光谱变化特征作为异常目标检测检测依据，弥补线性滤波抗噪性能差，难于区分复杂背景特征与目标特征的缺点。结果 仿真实验结果验证该算法在仿真数据及真实遥感数据的异常检测效果上有较大改善，在实现快速异常检测的同时提高了检测命中率。结论 本文方法不涉及背景建模，计算复杂度低，具有较好的实时性与普适性。特别是对复杂背景下的小尺寸异常目标具有较好的检测效果。

Anomaly detector based on bionic nonlinear filter for remote sensing data

Objective 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. Method 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. Result 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. Conclusion 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.