Research on the Classification of Complex Wheat Fields Based on Multi-Scale Feature Fusion

Abstract

This study uses UAV multi-spectral remote sensing images to carry out ground object classification research in complex wheat field scenes with diverse varieties. Compared with satellite remote sensing, the high spatial resolution remote sensing images obtained by UAVs at low altitudes are rich in detailed information. In addition, different varieties of wheat have different traits, which makes it easy to misclassify categories in the process of semantic segmentation, which reduces the classification accuracy and affects the classification effect of ground object. In order to effectively improve the classification accuracy of ground object in complex wheat field scenes, two Multi-Scale U-Nets based on multi-scale feature fusion are proposed. Multi-Scale U-Net1 is a network model that adds a multi-scale feature fusion block in the copy process between U-Net encoding and decoding. Multi-Scale U-Net2 is a network model that adds a multi-scale feature fusion block before U-Net inputs an image. Firstly, the wheat field planting area of Institute of Water-saving Agriculture in Arid Areas of China (IWSA), Northwest A&F University was selected as the research area. The research area was planted with a variety of wheat with various types of traits, and some traits were quite different from one another. Then, multi-spectral remote sensing images of different high spatial resolutions in the study area were obtained by UAV and transformed into a data set for training, validation, and testing of network models. The research results showed that the overall accuracy (OA) of the two Multi-Scale U-Nets reached 94.97% and 95.26%, respectively. Compared with U-Net, they can complete the classification of ground object in complex wheat field scenes with higher accuracy. In addition, it was also found that within the effective range, with the reduction of the spatial resolution of remote sensing images, the classification of ground object is better.