结合光谱和纹理的高分辨率遥感图像分水岭分割

A Watershed Algorithm Combining Spectral and Texture Information for High Resolution Remote Sensing Image Segmentation

针对遥感图像分割时仅利用光谱信息容易造成过分割和边缘定位不准的问题,提出一种结合光谱强度和纹理信息的遥感图像分水岭分割算法。首先分别提取图像的光谱梯度和纹理梯度,提出一种改进双边滤波模型,滤除图像中的噪声的周时,采用了一种局部的平滑尺度,能够有效消除纹理信息,借助于滤波算法,分别对原图像和Gabor纹理特征图像进行平滑处理,利用边缘检测算子得到光谱梯度和纹理梯度。最后利用形态学膨胀方法进行融合融合,使用分水岭变换对图像分割。用三幅高分辨率彩色遥感图像数据进行实验,并与JSEG(Joint Systems Engineering Group)和多分辨率分割方法进行比较,结果表明该方法具有较高的边界定位准确性,同时降低了过分割和欠分割现象。

High resolution remote sensing image segmentation methods that consider only the spectral information in the region growing process often lead to over segmentation and low boundary precision. To overcome that, a watershed transform algorithm which combines spectral information and texture information is proposed. At first, the spectral intensity gradient and the texture gradient have to be extracted from the input image. For that purpose, a new bilateral filtering model is introduced. This edge preserving algorithm can remove noise of images. Meanwhile, it can also remove texture from images by using a local smoothing scale parameter. By adapting this filtering algorithm on the original image and the Gabor texture feature images, the spectral information and texture information are ex- tracted separately. Then with edge detection algorithm, the spectral intensity gradient and texture gradient are obtained. Finally a gradient fusion strategy by morphological dilation and watershed transform are performed in succession. Experiments are carried out on three high resolution color re- mote sensing images. Compared with JSEG and multi-resolution segmentation methods, the proposed method has a higher boundary precision and can reduce the over segmentation and under segmentation effects.