Robust Cultivated Land Extraction Using Encoder-Decoder

Abstract

: Cultivated land extraction is essential for sustainable development and agriculture. In this paper, the network we propose is based on the encoder-decoder structure, which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation solutions. The encoder consists of two part: the first is the modified Xception, it can used as the feature extraction network, and the second is the atrous convolution, it can used to expand the receptive field and the context information to extract richer feature information. The decoder part uses the conventional upsampling operation to restore the original resolution. In addition, we use the combination of BCE and Loves-hinge as a loss function to optimize the Intersection over Union (IoU). Experimental results show that the proposed network structure can solve the problem of cultivated land extraction in Yinchuan City.