基于注意力机制的自适应滤波遥感图像分割网络

An adaptive filtering remote sensing image segmentation network based on attention mechanism

遥感图像尺度变化较大,背景类内差异较大以及前景和背景之间的不平衡等问题,增加了遥感图像小目标和目标边缘分割的难度。在卷积神经网络中,下采样引起的混叠效应造成目标信息的失真和损失,容易被忽视。同时,扩张卷积虽然捕获到了丰富的感受野信息,但仍存在冗余的背景信息干扰。据此,提出了一种基于注意力机制自适应滤波分割网络(ARGNet)。在DeepGlobe Road Extraction数据集和Inria Aerial Image Labeling数据集上进行实验,结果表明,所提出的网络能够分割出更加精准的目标。

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.