Multi-Scale Receptive Field Rectification of Remote Sensing Images

Remote sensing semantic segmentation refers to the pixel-level classification of high-resolution imagery obtained through remote sensing technology. In the era of deep learning, U-shaped network structures have been gaining popularity. These networks adopt a multi-level backbone to obtain multi-scale features with multiple receptive fields of different scales, which can make the network hierarchically understand contextual information. As the backbone goes deeper, the network is believed to obtain a larger receptive field. However, an in-depth effective receptive-field analysis reveals that such enlargement is unclear and the deepest receptive field is still local. Therefore, this paper conducts studies on the U-net structure and leverages dilated convolution to rectify the receptive field of multi-scale features. The effectiveness of the rectification is evaluated on the LoveDA benchmark and the rectified receptive fields are compared comprehensively with the non-rectified receptive fields.