粗糙集高分辨率遥感影像面向对象分类

面向对象的高分辨率遥感影像分类已受到研究者们的广泛关注。本文提出一种基于粗糙集理论的面向对象分类方法以区分高分辨率遥感影像上的不同地物。首先,利用基于相位一致梯度与前景标记的分水岭变换进行影像分割,提取图像斑块;然后,利用Gabor小波提取斑块的纹理特征,进而根据粗糙集理论提取纹理分类规则;最后,在对象光谱特征的初步分类结果,根据纹理分类规则得到最终结果基础上。依据粗糙集理论只能处理离散属性数据,本文重点提出一种适用于面向对象分类的连续区间属性离散化方法。实验表明本文方法可取得较好分类结果与较高分类精度。

Rough set theory based object-oriented classification of high\nresolution remotely sensed imagery

Object-oriented classification has been paid more attention in the field of remote sensing. In this paper, a novel object-oriented algorithm based on rough set theory is proposed to classify different objects extracted from high-resolution remotely sensed imagery. The method consists of three steps. Firstly, image segmentation is achieved by watershed transform based on phase congruency gradient and foreground marking to extract image objects. Secondly, texture vector of each object is obtained by Gabor wavelet, and clustering rules is further formed based on the knowledge reduction theory. Finally, according to the restriction of the preliminary clustering result derived from spectral feature of objects, the ultimate classification is achieved referring to the rules. Meanwhile, a new technique to discretize continuous interval-valued attributes is developed, which is very suitable for the object-oriented classification, because the rough set is inadequate for dealing with continuous attributes. The experiments demonstrate that the proposed method can achieve better results and better accuracies.