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中文摘要：
改进了传统的基于小波域隐马尔科夫树模型的图像分割方法．由于传统方法均为直接选择小波子带系数作为训练特征，不能直接得到像素级分割结果；同时传统方法在后融合方面对所有尺度均采用同一种上下文背景，而忽略不同尺度上初分割娄标志图的特点．因此，本文在粗分割阶段首先处理了训练时参数设置的问题，并选取了更能表征纹理的特征，能直接得到像素级分割结果；在多尺度融合阶段，充分利用不同尺度上类标志图的特性，不仅考虑粗尺度信息对融合结果的影响也考虑了细尺度信息对结果的影响．实验表明本文算法的视觉效果好于与本文进行比较的Choi提出的HMTseg以及孙强提出的WD—HMTseg遥感图像分割算法．
英文摘要：
A segmentation algorithm based on wavelet domain hidden Markov tree model was improved. The pixel level segmentation result can not be obtained because of choosing wavelet coefficients as training feature directly in traditional methods. At the same time, traditional methods ignore the feature of labeling maps at different scales by using one single context to all scales in fusion phase. Hence, this study dealt with the initial parameters set problem and chose better feature for training. In this way, the fine pixel level segmentation can be acquired directly in the raw segmentation step, and in multiscale fusion phase, the characteristics of labeling maps at different scales are used sufficiently. Among them, both the information from coarse-scale segmentation and the one from fine-scale segmentation were considered. Experiments show that the visual effects of our algorithm are the best compared with the HMTseg method proposed by Choi and the WD-HMTseg algorithm of remote sensing image segmentation presented by Sun Q.
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