Crop classification in high-resolution remote sensing images based on multi-scale feature fusion semantic segmentation model

The great success of deep learning in the field of computer vision provides a development opportunity for intelligent information extraction of remote sensing images. In the field of agriculture, a large number of deep convolutional neural networks have been applied to crop spatial distribution recognition. In this paper, crop mapping is defined as a semantic segmentation problem, and a multi-scale feature fusion semantic segmentation model MSSNet is proposed for crop recognition, aiming at the key problem that multi-scale neural networks can learn multiple features under different sensitivity fields to improve classification accuracy and fine-grained image classification. Firstly, the network uses multi-branch asymmetric convolution and dilated convolution. Each branch concatenates conventional convolution with convolution nuclei of different sizes with dilated convolution with different expansion coefficients. Then, the features extracted from each branch are spliced to achieve multi-scale feature fusion. Finally, a skip connection is used to combine low-level features from the shallow network with abstract features from the deep network to further enrich the semantic information. In the experiment of crop classification using Sentinel-2 remote sensing image, it was found that the method made full use of spectral and spatial characteristics of crop, achieved good recognition effect. The output crop classification mapping was better in plot segmentation and edge characterization of ground objects. This study can provide a good reference for high-precision crop mapping and field plot extraction, and at the same time, avoid excessive data acquisition and processing.