A Background Self-Learning Framework for Unstructured Target Detectors

Unstructured background model based detectors have been successfully applied in various hyperspectral target detection applications. The background statistics of an image can be estimated in a global way or a local way. The global approach involves modeling the background directly from the whole image, which can prove to be inaccurate due to target contamination of the background information. The local approach usually involves estimating the background statistics using a spatially sliding local window. However, this approach can also fail to reflect reality, due to sensitive parameters, like the window size, and presents high computational costs. This letter proposes a self-learning method to adaptively determine the background statistics for unstructured detectors, with the consideration of exploiting both the spatial and spectral information, and accelerating the computation speed. The experimental results with two real hyperspectral images confirm the superior performance when compared to the other two approaches to modeling background statistics.