基于字典重构的高光谱图像亚像元目标检测

Sub-pixel target detection on hyperspectral image based on dictionary reconstruction

稀疏表示的引入为高光谱遥感图像的目标检测提供了新途径,但在其检测过程中,由于过完备字典的构造是直接从高光谱图像中进行获取的,存在不确定性因素且无法实现对亚像元的准确检测。针对上述问题,本文提出了一种基于字典重构的高光谱图像亚像元目标检测算法。该算法利用无监督方法进行过完备字典的构造,确保过完备字典中包含部分目标像元的光谱信息,同时引入二元对立假设模型实现对高光谱图像中亚像元目标的检测。对模拟及真实高光谱遥感图像数据进行实验仿真,通过对实验结果三维图、ROC曲线以及AUC值的对比分析,得出本文所提出的算法,该算法不仅提高了检测精度而且更好地抑制了背景噪声。

The introduction of sparse representation provides a new way for the target detection of hyperspectral remote sensing images.However,in the detection process,because the structure of an over-complete dictionary is obtained directly from the hyperspectral image,there are uncertainties and it is hard to realize the accurate detection for the sub-pixels.In order to solve the above-mentioned problems,this paper proposed a sub-pixel target detection algorithm on hyperspectral image based on dictionary reconstruction.In the algorithm,an unsupervised method is used to complete the construction of an over-complete dictionary,so as to ensure that the dictionary contains the spectral information of partial target pixels;meanwhile,binary alternative hypothesis model is introduced to detect the sub-pixel target in hyperspectral image.Simulation experiments were carried out respectively for the simulative and real hyperspectral remote-sensing image data.By carrying out comparison and analysis for the three-dimensional drawing,ROC curve and AUC value in the test results,it is known that the algorithm proposed in the paper not only increased the detection precision,but also properly constrained the background noise.