Abstract: Due to the large-scale changes of remote sensing images, large intra-class differences in the background, and the imbalance between the foreground and the background, it is difficult to segment the small objects and object edges of remote sensing images. In convolutional neural networks, the aliasing effect caused by downsampling causes the distortion and loss of object information, which is easily ignored. At the same time, although the expanded convolution has captured rich receptive field information, there is still redundant background information interference. Accordingly, an adaptive filter segmentation network (ARGNet) based on an attention mechanism is proposed. Experiments on the DeepGlobe Road Extraction dataset and the Inria Aerial Image Labeling dataset show that the proposed network can segment more accurate objects.

Key words: convolutional neural network, remote sensing image segmentation, adaptive filtering, attention mechanism, feature fusion