多源数据结合的高分一号土地利用/覆盖分类方法研究

基于多时相的GF-1数据获取NDVI时序变化、NDWI和MNDVI等指数图像数据，辅以Landsat8卫星OLI影像和数字高程模型(DEM)数据，得到了不同地物在光谱、时相和形状等方面的特征；通过分析各种地物类型在这些特征上的差异和变化规律，总结出不同地物的特征提取规则，构建了一种基于GF-1数据在地物复杂地区的土地利用/覆盖分类方法，并以广州市为实验区，运用该方法、最大似然法和最小距离法进行了土地利用/覆盖分类及其精度评价.结果显示:基于GF-1数据在地物复杂地区的土地利用/覆盖分类方法的总体精度为85.86%(部分地物分类精度达到95%以上)，与最大似然法及最小距离法相比，其总体精度分别提高了4.62%和12.24%，说明该方法能够更好地发挥GF-1遥感数据在土地利用/覆盖分类中的实际应用潜力，且有效提高了各种土地利用/覆盖地物类别的分类精度.

Research on Land Use/Cover Classification Based on GF-1 and Multi-Source Data Combination

Based on the NDVI time-series data, NDWI data, MNDWI data and some other index data which were obtained from the GF-1 multi-temporal data as well as the Landsat8 OLI images and DEM data, the rules of extracting the spatial, multi-temporal and shape features of land objects were derived. A land use/cover classification method of complex terrains based on the GF-1 data was constructed according to those rules and the multi-layer information extraction method. With Guangzhou as the test area, the methods as well as the maximum likelihood, the minimum distance method and the land use/cover classification method of complex terrains based on the GF-1 data were used to class the land used/cover. The results showed that the overall accuracy of classifying the use/cover of land of complex terrains based on the GF-1 data is 85.86%. Besides, the accuracy of extraction of some land objects goes higher than 95%. Compared with the maximum likelihood method and the minimum distance method, this method increases the accuracy by 4.62% and 12.24% respectively, which shows that it can improve the application of GF-1 data in land use/cover classification and increase its accuracy.