针对深空探测影像纹理贫乏导致的匹配不确定性问题，在传统马尔科夫匹配模型的基础上提出自适应马尔科夫随机场模型，将模型抽象为数据项和空间项的组合，通过自适应视差范围确定、自适应窗口匹配和自适应权重系数的有效结合，实现了深空探测影像的密集匹配。该模型在匹配过程中有效降低了视差搜索范围，在实现全局匹配的同时保留了视差不连续的区域特征。采用勇气号火星车影像与嫦娥影像进行实验，结果证明该方法提高了深空探测影像中纹理贫乏区域匹配的精度。

This paper proposes an adaptive Markov Random Field (aMRF) model for the dense matching of deep space exploration images, which usually lack textures. Compared with traditional MRFs, the approach improves dense matching accuracy by a combination of adaptive disparity range predictions, adaptive matching windows, and adaptive weight coefficients. Furthermore, aMRF reduces the disparity search range while accurately preserving disparity discontinuities. Real rover images from the Mars Exploration Rover mission and Chang'E lunar orbiter images experiments demonstrate the effectiveness of the proposed method.