一种自适应多层结构和空谱联合的高光谱图像异常检测方法

Adaptive multi-layer structure with spatial-spectrum combination for hyperspectral image anomaly detection

针对传统高光谱图像异常检测算法大多只考虑异常点与背景像素的光谱差异、忽略二者空间结构差异等问题,提出一种基于局部空间结构差异的自适应多层结构空谱联合异常检测方法。该新方法计算待测像素与背景窗像素在光谱维度上的差异以及内窗与背景窗在空间结构上的差异,重点构建了一种自适应多层结构的异常检测框架。该框架基于背景抑制的思想,构建多层级联的异常检测器,将每一层检测器的异常检测结果作为约束,抑制下一层检测器中输入图像的背景信息,从而自适应地完成背景抑制。实验结果表明,所提方法较传统的双窗模型(包括全局RX、局部RX和KRX)更好地利用了局部空间结构和光谱维度信息,自适应地对背景进行抑制,降低了虚警率,提升了对尺寸较小的异常目标的检测效果。

A new algorithm for hyperspectral image anomaly detection is proposed by designing an adaptive multi-layer structure with spatial-spectral combination information,which is different from the traditional anomaly detection algorithms only considering the spectral difference between the anomaly point and the background pixels,and ignoring the difference between the local spatial structure and spectrum.Firstly,the present algorithm not only calculates the spectral dimension difference between the pixels to be measured and the pixels in the background window,but also measures the spatial structure difference between the internal window and the background window.Mostly,an adaptive multi-layer structure for anomaly detection framework is carried out based on the idea of background suppression,and a multi-layered anomaly detector is constructed.The anomaly detection results of each layer of the detector are taken as the constraints,and the background information of the image input in the detector of the next layer is suppressed,adaptively suppressing the background noises.The experimental results show that the present algorithm makes better use of both the local spatial structure and the spectral dimension information than the traditional two-window models(global RX,local RX and KRX),adaptively suppresses background,reduces the false alarm rate,and improves the detection effect of the abnormal targets with fewer pixels.