关键词: 主成分分析（PCA）, 分割的主成分分析（SPCA）, 域变换递归滤波（DTRF）, 高光谱图像分类, 基本阈值分类器

Abstract:

A new hyperspectral image classification method is proposed based on Segmented Principal Component Analysis（SPCA） and Domain Transform Recursive Filtering（DTRF）. First, the SPCA method is used to reduce the dimension of hyperspectral image and extract the first principal component of each band subset. Then, DTRF with different parameters deals with the first principal component of each band subset to form a stacked edge-preserving filter map. And the Principal Component Analysis（PCA） is used to fuse the features of the stack-preserving filter map. At last, the Basic Thresholding Classifier（BTC） classifies the fused principal components. Simulation experiments show that the proposed method can improve the classification accuracy, and the overall accuracy, average accuracy and Kappa coefficient are higher than the some existing methods.

Key words: Principal Component Analysis（PCA）, Segmented Principal Component Analysis（SPCA）, Domain Transform Recursive Filtering（DTRF）, hyperspectral image classification, basic thresholding classifier