加入空间纹理信息的遥感图像道路提取

由于道路与建筑物等其他不透水层存在光谱相似性,导致仅利用光谱信息进行道路提取的效果不佳.本文针对高等级城市道路目标,提出了一种加入空间纹理信息的遥感图像道路提取方法.首先,对图像进行空间自相关Moran指数计算,提取图像空间纹理信息,并将其加入到原始光谱波段中;其次,通过建立知识模提取假设道路段,并对提取结果进行假设验证;最后,采用数学形态学的方法对验证后的结果进行后处理.以空间分辨率为0.1m的航空影像为数据源,对本方法进行实验.实验结果表明,加入空间纹理信息的遥感图像道路提取精度总体达到88%,比不加入空间纹理的提取精度要提高约5%.

Road Extraction from High-resolution Remote Sensing Imagery by Including Spatial Texture Feature

The methods using spectral information alone are often ineffective due to the spectral similarity between roads and other artificial structures with impervious surface. This paper proposed a knowledge-based method for urban road extraction by including spatial texture information. The spatial texture feature was firstly extracted by the local Moran''s I and the derived texture was added to the spectral bands of images for image segmentation. Then, features like brightness, standard deviation, rectangularity, aspect ratio and area were selected to form the hypothesis and verification model. Finally, roads were extracted by applying the models and were post-processed on the basis of mathematical morphology. This new method was evaluated by a 0.1m aerial image. The results show that the extraction accuracy reaches about 88% by using the proposed method, 5% higher than the corresponding images without the spatial texture information.