针对单一滤波器提取高光谱图像空间特征时不能获得完整的图像空间信息的不足, 提出一种结合非局部均值滤波和导向滤波的高光谱图像分类算法。该方法利用非局部均值滤波提取高光谱全波段图像空间信息, 利用导向滤波提取经由主成分分析(PCA)降维后的高光谱图像的空间边缘信息, 将两种空间信息进行线性融合的结果输入至支持向量机(SVM)完成分类。实验表明, 相比于使用光谱信息、高光谱PCA降维、空谱结合的SVM分类、边缘保持滤波以及递归滤波等方法, 该算法能够有效提高光谱图像的分类精度。

The full spatial information cannot be obtained by single filter in characteristics extraction of hyperspectral image. We propose a classification method which combines two kinds of information extracted by non-local means filter and guided filter. This method advances a fusion of spatial information for hyperspectral image classification. One kind of spatial information for all bands of hyperspectral image is extracted by the non-local means algorithm, and another kind of spatial information is obtained by guided filter for the same image after reducing dimensionality with principal component analysis (PCA). Two kinds of spatial information are combined, and the classification is done by support vector machine (SVM). Experimental results show that the proposed algorithm is better than the spectrum information, PCA dimensionality reduction, spatial-spectral SVM, edge-preserving filtering and recursive filtering methods, and the classification accuracy of hyperspectral image is effectively improved.