针对多时相、多分辨率遥感影像数据的特点，充分考虑不同分辨率数据和不同变化检测应用的需求，将由粗到精数据集分层检测和决策级融合的思想引入到变化检测，以多时相多分辨率ALOS遥感影像为例，构建并试验了由粗到精变化检测的技术流程。该方法将ALOS多光谱数据视为粗数据集，将全色数据和融合数据视为精数据集，通过对3种数据集变化检测结果按照一定的决策规则进行综合，生成最终的变化检测结果图，反映变化的发生位置及变化强度。选择江苏省徐州市城区和矿区两个区域进行试验并与常规变化检测算法结果进行对比，表明该方法具有更好的检测效果，可以有效地应用于多分辨率遥感影像变化检测，并为实际野外调查提供重要的检测靶区。

According to the characteristics and merits of multi-resolution remotely sensed images and objectives of different change detection applications, the idea using coarse to fine (CTF) hierarchical detection and decision level fusion are introduced into change detection process. The technical flow of change detection based on CTF is designed and implemented with multi-temporal ALOS images as the experimental data. The four band ALOS multi-spectral images are viewed as coarse resolution data, and the panchromatic image and fusion image of multispectral and panchromatic data are viewed as fine data. After processing the fine and coarse datasets individually, results are integrated to form a new dataset which reflects the location and intensity of final changes based on specific fusion rules. Land cover change detection of two study case areas over the urban area and mining area of Xuzhou City are conducted. Comparing the proposed approach with other common methods, it is concluded that the proposed CTF decision level fusion change detection approach outperforms other traditional algorithms, and it is effective to conduct change detection using multi-resolution remotely sensed images, further providing the important target change areas of field work.