Method and device for classifying remote images by integrating edge information and support vector machine

Abstract

The invention relates to a method and a device for classifying remote images by integrating edge information and a support vector machine. The method includes the steps: performing pixel-wise support vector machine classification for the remote images which are subjected to preprocessing and characteristic extraction; performing edge detection for the remote images which are subjected to preprocessing and characteristic extraction so as to obtain a discontinuous one-pixel wide edge map; performing edge connection for the discontinuous one-pixel wide edge map subjected to noise edge removal so as to obtain a closed edge map; and integrating the closed edge map to a map subjected to pixel-wise support vector machine classification so as to obtain a classified result map integrating the edge information. By the aid of the method and the device, non-robustness of existing partitional-clustering-segmentation-based classification of the hyperspectral images by integrating space and spectral information is overcome, and the problem of dimensional proportion selection of common fixed-window-size-based methods such as morphology filtering by integrating the space and spectral information is solved. 本发明涉及一种融合边缘信息和支持向量机对遥感图像进行分类的方法及装置，该方法包括：对经过预处理和特征提取的遥感图像进行逐像素的支持向量机分类；对所述经预处理和特征提取的遥感图像进行边缘检测，以获得非连续的单像素宽边缘图；对经噪音边缘部分去除的所述非连续的单像素宽边缘图执行边缘连接处理，以获得封闭的边缘图；将所述封闭的边缘图集成到经逐像素的支持向量机分类的图中，以得到融合边缘信息的分类结果图。本发明解决了现有的基于划分聚类(Partitional Clustering)分割的高光谱图像融合空间及光谱信息分类的非稳健性，也解决了通常的基于固定窗口大小的融合空间及光谱信息分类方法例如形态学滤波的尺寸比例的选择问题。