提出了一种新的基于三维光谱角统计的光谱图像信息提取方法。 通过对影像上横向、 竖向和对角方向相邻像元间采用光谱角计算相似度, 构成一个三维的信息统计模型。 该模型反映了相邻像元间所代表物质的相似度, 通过在统计模型中设置不同的阈值和提取不同轴向的切片, 可以从影像中提取代表同种物质的均匀区域和边缘信息, 用于监督分类中训练样本的采集。 该统计方法与直方图、 散点图等传统统计工具相比, 鲁棒性和可靠性更高, 提取的信息更丰富。

A novel information extraction method of spectral images based on 3D spectral angle statistics is proposed. By computing the spectral angle between adjacent pixels of the image in the horizontal, vertical and diagonal direction respectively, a 3D information statistical model was then constructed. This model reflects the similarity between adjacent pixels which represent some kind of materials. Uniform areas and edge information of the same material, which will be used for the training sample collection in supervised classification, can be extracted from the image if different threshold values are set and slices are extracted from different axes in the statistical model. Compared with the traditional statistical tools, such as the histogram and scatter diagram, this statistical method has higher robustness and reliability. And it can obtain more information extracted from the spectral images.