结合双树复小波纹理特征和MRF模型的遥感图像分割

针对经典的小波纹理不能准确地表达影像纹理特征的问题,以及影像分割结果缺少对像元空间相关性和分布关系的考虑。本文提出了结合双树复小波(DT-CWT)纹理和马尔可夫随机场(MRF)模型的高分辨率遥感影像分割方法。首先,通过双树复小波变换提取影像纹理特征,联合光谱特征形成表达影像信息的混合特征向量;然后,将混合特征向量高斯归一化处理,并用K-means聚类的方法对特征空间中的混合特征向量聚类得到初始分割图;最后,借助马尔可夫随机场模型在初始分割结果中引入上下文信息,基于贝叶斯最大后验概率准则得到最终的分割结果。本文通过双树复小波纹理提高了特征表达的准确度,同时使用马尔可夫随机场模型减弱了分割结果中同质区域的“椒盐噪声”,从而进一步提高了高分辨率遥感影像分割的精度。

Remote sensing image segmentation method based on dual-tree complex wavelet texture feature and MRF model

For the classic wavelet textures, the image texture features cannot be accurately expressed. The image segmentation results lack the consideration of the spatial correlation and distribution relationship of the pixels. In this paper, we proposes a combination of dual-tree complex wavelet (DT-CWT) texture and Markov random field (MRF) model for high resolution remote sensing image segmentation method. Firstly, the image texture feature is extracted by dual-tree complex wavelet transform, and the mixed feature vector of the expression image is formed by combining the texture feature and the spectral feature. Then, Gaussian normalization of the mixed feature vectors in the feature space is performed. The K-means clustering method is used to perform the feature vectors in the feature space. The initial segmentation map is obtained by clustering. Finally, using the Markov random field model to introduce context information to represent the initial segmentation results, the initial segmentation results are optimized based on the Bayesian maximum a posteriori probability criterion, and the final segmentation results are obtained. This paper improves the accuracy of feature expression by using dual-tree complex wavelet textures. At the same time, the Markov random field model is used to weaken the pepper and salt noise in the homogenous region of the segmentation results, further improving the segmentation accuracy of the high resolution remote sensing image.