A Method for Automatic Land Classification of UAV Digital Orthophoto Map
The land classification of UAV digital orthophoto map still mainly depends on human-computer interaction,which requires ex-perienced and qualified operators,with low efficiency and high cost. Therefore,a method for automatic land classification of UAV Digital Or-thophoto Map was proposed,using VGG Image Annotator to divide the land types in the image into minimum units to obtain refined samples;building a Mask R-CNN network with ResNet 50 as the feature extractor;training and testing the network using pre-trained models and land type samples. Sample set was produced using UAV digital orthophoto image of a certain region,with 1m resolution,and a training and testing environment was built based on TensorFlowgpu 1. 11. 0 and Keras 2. 0. 9. The test results show that the F1 value of this method for identifying four land types,including house,plowland,forest and water,can reach over 70％,which demonstrates that the new method was feasible.
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