Winter Wheat Extraction Method Based on Improved Convolution Neural Network
In order to extract winter wheat from high-resolution images with high accuracy,the traditional convolutional neural network were improved,and the implemented neural network Im SegNet(Improved SegNet)is got.The model adds the difference information of the probability vectors of winter wheat and non-winter wheat,and makes a secondary judgment on the pixels with smaller difference of the probability vectors,which improves the extraction accuracy of the convolution neural network model.77 GF-2(Gaofen 2)images of winter wheat in Feicheng City,were collected and used as experimental data,and three indicators of accuracy,accuracy and recall aws used to verify and evaluate the extraction effect of Im-SegNet model.The accuracy of the extraction results of the Im-SegNet model is 92.1％,the accuracy rate is 91.8％,and the recall rate is 84.9％.The three indicators are higher than the extraction results of the classic SegNet model,indicating that the Im-SegNet model is more suitable for extracting the spatial distribution information of winter wheat from high-resolution images.
full convolutional neural networkremote sensing imagebayesianwinter wheat
NETL
NSTL
万方数据
维普
