It is of great significance to study the method of extracting urban features from GF-2 remote sensing data.Taking the urban area of Jixi City as the study area，and the GF-2 image is used as the data source.The image is divided into multiple scales，the classification rules of the corresponding objects are established，and the object-based classification method of the rule set is used to classify the objects.Compare with SVM supervised classification results.The results show that the overall accuracy of object-oriented classification is 92.52%，and the Kappa coefficient is 0.91，which is significantly higher than the SVM supervised classification.Using the object-oriented classification method to classify the GF-2 image is better and the precision is higher.Object-oriented classification method based on GF-2 data is an effective method for extracting urban land use classification.
宋明辉. 基于高分二号数据的面向对象城市土地利用分类研究[J]. 遥感技术与应用, 2019, 34(3): 547-552.
Song Minghui. Object-oriented Urban Land Classfication with GF-2 Remote Sensing Image. Remote Sensing Technology and Application, 2019, 34(3): 547-552.
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