In the geological disaster monitoring system, the image change detection can be used to monitor the occurred disaster. Image matching is needed to change detection accurately. Aiming at the problem of low matching precision with the image in the fog condition, an image matching method based on image contrast enhancement of SURF is proposed, enhanceing the matching precision of the fog image efficiently. It uses multi-scale Retinex algorithm to enhance the fog image, and then completes the extraction of feature points with SURF algorithm, uses Euclidean distance to measure the similarity of characteristic points, according to the similarity triangular distance ratio invariance, it deletes point pairs of mismatch. Experimental results show that the method significantly improves the precision of image feature points match-ing, provides a good foundation for subsequent use of image change detection for disaster monitoring.%在地质灾害监测系统中，可以通过图像的变化检测对发生的灾害进行监测。要精确地进行变化检测，需要首先对图像进行匹配。针对雾天图像匹配精度低的问题，提出了一种基于雾天图像增强的SURF（Speed Up Robust Feature）图像匹配方法，有效地提高了雾天图像的匹配精度。用MSR（Multi Scale Retinex）算法对雾天图像进行增强处理，采用SURF算法完成特征点的提取，用欧式距离来度量特征点的相似度，根据相似三角形的距离比例不变性删除误匹配的点对。实验结果表明，该方法显著提高了图像特征点的匹配精度，为后续利用图像变化检测进行灾害监测提供了良好的基础。
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