MMUU-Net: A Robust and Effective Network for Farmland Segmentation of Satellite Imagery

作者：Xumin Gao, Long Liu, Huaze Gong · 发表于：Journal of Physics, Conference Series · 年份：2020 · DOI：10.1088/1742-6596/1651/1/012189 · 被引用次数：10 · 研究领域：Physics、Computer Science

Aiming at the multi-scale characteristics of satellite imagery, and the adhesion phenomenon in the farmland segmentation results which is caused by the close distance between different farmland blocks, this paper proposes a robust and effective network based on U-Net for farmland segmentation of satellite imagery, which is called MMUU-Net. On the basis of adopting the encoder with higher classification accuracy network, adding ASPP (Atrous Spatial Pyramid Pooling) layer in the middle, and designing the multi-scale feature fusion module in the decoder, so that the multi-scale feature information is fully utilized; in order to better fuse multi-scale information, a more robust loss function is designed; finally, we propose a segmentation strategy of the coarse and refined two-stage to eliminate the adhesion phenomenon. Through the comparative experiments, it is verified that MMUU-Net is better than other segmentation networks, and can be effectively applied to the task of farmland segmentation of satellite imagery.