基于遥感图像分割和形态学的黄土滑坡后壁自动提取方法

基于黄土滑坡后壁的光谱和形状特征,提出了利用区域生长合并方法分割高分辨率遥感影像,提取滑坡后壁区域,经过二值化构图处理后,获得滑坡后壁主要格架,以表征黄土滑坡的方法。首先充分考虑黄土滑坡后壁在遥感影像中所呈现出的独特光谱特征和半圆状或圈椅状的形状特征,利用区域生长合并的方法,在生长参数为47.5,合并参数49的状态下进行影像分割,获得初步的黄土滑坡后壁形状;其次,填充分割过程中产生的孔洞,对获得的滑坡后壁进行细部构图;最后,采用二值化骨骼提取算法,提取滑坡后壁的主要格架,作为黄土滑坡的标志。该方法是对黄土滑坡遥感解译工作中,滑坡表达方法的改进和提高。

Identification and extraction of loess landslide scarp based on remote sensing image segmentation and mathematic morphology method

Based on the spectral and shape features of loess landslide scarp, the authors segmented the high resolution remote sensing image by using regional growing and merging method, and extracted the landslide scarp area from the segmented image. The skeleton of the scarp area was obtained by way of the binary composition to characterize loess landslide. Firstly, The typical semi-circle or round-backed armchair shape characteristics and spectral characteristics became one of the important marks for loess landslide remote sensing interpretation, whereas the initial shape of the scarp was achieved by taking advantage of the regional growing and merging method with the growing parameter being 47.5 and the merging parameter being 49. Then, the holes of initial scarp of the scarp generated by segmentation process were filled. Finally, the main framework of landslide scarp was extracted using binarization skeleton algorithm, which constituted the mark of the loess landslide. This method is an improved and enhanced means for loess landslide expression in the remote sensing interpretation.