研究了一种结合高分辨率遥感影像和GIS数据的土地利用自动变化监测方法。分别采用马氏距离、支持向量机、神经网络3种分类方法对研究区域进行模糊分类，然后通过多分类器模糊决策融合分类方法对3种分类结果进行融合，提高总体分类精度。在获得高精度分类结果的基础上，结合历史时期GIS专题数据和分类结果，计算对应的图斑多边形内各类别成份，并与其历史属性对比分析，自动实现土地利用变化监测。实验数据选取Quick Bird高分辨率遥感影像和1∶1万土地利用GIS数据，实验结果表明模糊决策融合分类能够明显改善分类效果，获得比单一分类器更为精准的结果。利用决策分类结果并结合GIS数据进行变化区域判定实验，结果表明,对区域中存在较大变化能够准确自动判定为变化区域，而区域中存在较少部分变化则存在部分误判现象。

An automatic landuse change detection approach combining high spatial resolution remotely sensed image with GIS data is proposed. Mahalanobis distance, Support Vector Machine (SVM) and Neural Network (NNT) are used respectively for fuzzy classification. To improve the overall accuracy of classification,  a fuzzy decision fusion classification algorithm is designed to combine each fuzzy classification result from the above methods.  Based on these accurate classification results and the GIS data in historical period, each classification in the corresponding polygon is calculated. The changed area is automatically detected by comparing the calculated land status ratio with its historical status. QuickBird images and GIS data are taken for an experiment. Polygon which exhibits big change of land status ratio can be automatically detected and judged accurately; while polygon which has little change may be subject to false judgment. Experimental results proved that fusion classification results are more accurate than the individual classification result.