空间信息自适应融合的高光谱图像分类方法

针对单一的滤波器提取高光谱图像空间纹理信息时不能获得完整的图像特征的不足,提出一种结合双边滤波和域转换标准卷积滤波的高光谱图像分类算法.该方法采用空间信息自适应融合的分类寻优,先对高光谱波段进行抽样分组,再用双边滤波和域转换标准卷积滤波对分组后的波段进行滤波,两种空间信息进行线性融合后交由支持向量机完成分类.实验表明,相比使用光谱信息、高光谱降维、空谱结合的支持向量机分类方法和边缘保持滤波以及递归滤波的方法,本文所提算法对高光谱图像的分类精度有较大提高,在训练样本仅为5%和3%的情况下,对印第安农林和帕维亚大学图像的总体分类精度分别达到了96.95%和97.89%,比其他算法高出213个百分点,验证了该方法在高光谱图像分类的有效性.

Hyperspectral Image Classification Method Based on Adaptive Fusion of Spatial Information

The full characteristics cannot be obtained by single filter in spatial information extraction of hyperspectral image.Combining bilateral filter and domain transform filter of normalized convolution, an improved algorithm of classification was proposed.The method advanced an adaptive fusion of spatial information for classification optimization.Firstly, bands of hyperspectral image were sampled into two groups.Secondly, spatial information of the two group images was extracted by the bilateral filter and the normalized convolution respectively.finally, the two kinds of spatial information were combined and classified by support vector machine.The experiments show that the algorithm is better than original support vector machine with the pure spectrum information, dimensionality reduction, the spatial-spectral information, and the method of edge-preserving filtering and recursive filtering.the performance of hyperspectral image classification algorithm is greatly improved, although training samples were only 5% and 3%, the verall accuracy of Indian and Pavia can reach 96.95% and 97.89% respectively, with 2%~13% higher than other algorithms, and the effectiveness of the method is fully verified.