基于SPCA和域变换递归滤波的高光谱图像分类

提出一种基于分割的主成分分析（Segmented Principal Component Analysis，SPCA）和域变换递归滤波（Domain Transform Recursive Filtering，DTRF）的高光谱图像分类算法。利用SPCA方法降低高光谱图像的维数和提取各波段子集的第一主成分。使用不同参数的域变换递归滤波器对各波段子集第一主成分进行滤波，形成堆叠的边缘保持滤波图。采用主成分分析（Principal Component Analysis，PCA）将堆叠的边缘保持滤波图进行特征融合。利用基本阈值分类器（Basic Thresholding Classifier，BTC）对融合后的主成分进行分类。仿真实验表明，所提方法能够提高分类精度，且在总体分类精度、平均分类精度、Kappa系数等方面优于已有方法。

Hyperspectral Image Classification Based on SPCA and Domain Transform Recursive Filtering

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.