基于背景重建的高光谱图像异常检测

Hyperspectral Image Anomaly Detection Based on Background Reconstruction

针对高光谱图像异常检测中背景信息与异常目标信息难以有效区分,背景预测精度不佳的问题,提出一种新的基于背景重建的高光谱图像异常检测算法。通过字典学习方法获取高光谱图像背景光谱字典,并利用该字典对待检测图像进行稀疏重建,得到预测背景图像。将预测背景图像与原始图像做差后得到残差图像,进而利用局部RX检测算法对残差图像进行遍历,实现异常目标检测。通过对真实高光谱图像场景进行实验,证明了算法的有效性。

In the anomaly detection of hyperspectral images(HSIs),aiming at the difficulty of distinguishing the abnormal target from the background and the low accuracy of background prediction,a new HSI anomaly detection algorithm based on background sparse reconstruction is proposed.An online dictionary learning method is used to estimate the background spectral dictionary.The estimated background image is sparse reconstructed by the learning dictionary.The estimated background image is subtracted from the origin image to get the residual image.The anomaly detection is achieved by using the local RX detector to traverse the residual image.The effectiveness of the proposed HSI anomaly detection algorithm based on the background sparse reconstruction is illustrated in a series of real-world data experiments.