遥感图像分割的迭代上下文融合小波域HMT模型

由于已有小波域HMT(hidden Markov tree)图像分割算法在上下文融合阶段直接对数据块大小不等的相邻两尺度进行信息融合,导致细节信息分割不充分。为此,提出一种基于迭代上下文融合的小波域HMT模型图像分割算法。该算法在上下文融合阶段采用迭代融合方法,将每一尺度的融合结果作为该尺度的上下文信息再次融合,并设置变化阈值作为迭代终止条件。利用Brodatz纹理组合图像和Formosat-2遥感图像进行分割试验。定性和定量分析表明本文算法能改善图像分割的细节效果,进一步提高图像分割精度。

Wavelet-domain HMT Model Based on Iterative Context Fusion for Remote Sensing Image Segmentation

The wavelet-domain hidden Markov tree (HMT) model can capture multiscale texture features of objects in remote sensing imagery to obtain high quality segmentation. However, the existed wavelet-domain HMT segmentation methods directly fuse information of dyadic squares that are with different sizes at adjacent scales, resulting in the failure to get detail-preserving segmentation results. Therefore, a wavelet-domain HMT model based on iterative context fusion for image segmentation is proposed. In the proposed algorithm, context-based interscale information fusion is carried out in an iterative way. The current fused result at a scale is treated as the contextual information of the current scale to be refused in each loop, and the procedure stops when the difference between two successive iterations is smaller than the specified change threshold. Segmentation experiments are done using a synthetic image composed of Brodatz textures and a remote sensing image from Formosat-2 satellite, and quantitative and qualitative evaluation demonstrates that the proposed algorithm can preserve details better than traditional methods and achieve better segmentation performance.