基于多特征多分辨率融合的高光谱图像分类

由于数据维数高，利用高光谱数据对地物进行分类，常规方法难以获得令人满意的结果，在基于小波多分辨率融合方法进行特征图像的提取过程中，提出了利用多个空间特征所构成的特征矢量确定多分辨率融合权值的算法，有效地降低了原始图像的数据维并获得了用于后续分类的特征图像．对AVIRIS数据进行的实验表明，利用新方法提取的特征进行分类，获得了高于传统方法确定融合权值的结果。

HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON MULTIPLE FEATURES DURING MULTIRESOLUTION FUSION

Because of the high data dimensionality of hyperspectral data, conventional methods are difficult to obtain satisfied results in the study of hyperspectral classification for materials on the ground. In the process of feature images extraction based on wavelet multiresolution fusion, a new method, which uses a feature vector consisting of multiple spacious salient features to determine fusion weights, wass presented. The algorithm can effectively reduce the hyperspectral data dimensionality and obtain the feature images for the successive classification. The experiments on AVIRIS data show that classification accuracy by using the new method is higher than that of using the conventional methods in determining weights.