关键词: 建筑物, ResNeXt_Unet, 深度学习, GF-2, Douglas Peucker

Abstract: Aiming at the problem that the bilinear interpolation and transposed convolution algorithm of fully convolutional neural network cannot accurately restore the outline of the segmented object in the task of high-resolution remote sensing image building extraction, an improved ResNeXt_SPP_Unet fully convolutional neural network is established based on the Unet network, and it proposes improved Douglas Peucker image post-processing algorithm to complete building extraction regularization. The ResNeXt_SPP_Unet network focuses on two aspects of optimization. One is to replace the standard convolution in Unet with ResNeXt Block, which reduces the number of model operations and improves the segmentation accuracy of the network; the other is to introduce the SPP pyramid pooling layer at the end of the Encoder. The method of scale feature fusion improves the segmentation accuracy of object edges. Through experimental comparison and analysis, the results show that the improved ResNeXt_SPP_Unet is superior to classical segmentation networks such as Unet and cutting-edge segmentation networks such as ResUnet++ in the task of high-scoring remote sensing image building extraction, with an average intersection ratio of 0.853 8 and an average pixel accuracy of 0.935 9. Finally, after connecting the improved Douglas Peucker algorithm to the ResNeXt_SPP_Unet model, the algorithm is improved by adding processing operations such as rotation and connection of the edge of the building outline, further fitting the real outline of the building, and regularizing the boundary of the building. It works well.

Key words: buildings, ResNeXt_Unet, deep learning, GF-2, Douglas Peucker