Abstract

Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis (PCA) feature space. In this method, using the implantation of the target spectrum (TS) in the HSI and following the simulated targets in the PCA feature space, the best principal components (PCs) are selected. Then, using the mixing and unmixing coefficients of the PCs, a new TS and a new image in the PCA feature space are created. Afterwards, using the new spectrum of the target, the TD algorithm is run on the new HSI. The performance of the proposed method is compared to nine counterpart algorithms on Hymap and Hyperion HSI. All the comparisons are performed using adaptive coherence estimator (ACE) TD algorithm. Experimental results illustrate that the proposed method, compared to its counterparts, yields superior performance based on the false alarm rate (FAR) measure. It gives an average FAR value of about 16, which is approximately 9% better than that of its best counterparts.

摘要

目标检测（TD）是高光谱图像（HSI）处理中的一项主要任务，由于高光谱分辨率，需要处理维数灾难。集成的特征提取和选择是 HSI 降维的著名解决方案。在这项研究中，提出了一种新的方法来提高基于主成分分析（PCA）特征空间的TD算法的性能。在该方法中，通过在 HSI 中植入目标谱 (TS)，并在 PCA 特征空间中跟随模拟目标，选择最佳主成分 (PC)。然后，使用 PC 的混合和解混合系数，在 PCA 特征空间中创建一个新的 TS 和一个新的图像。之后，使用目标的新频谱，在新的 HSI 上运行 TD 算法。将所提出方法的性能与 Hymap 和 Hyperion HSI 上的九种对应算法进行了比较。所有比较均使用自适应相干估计器 (ACE) TD 算法进行。实验结果表明，与同类方法相比，所提出的方法在误报率（FAR）测量的基础上产生了优越的性能。它给出的平均 FAR 值约为 16，比最佳同类产品高出约 9%。