Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (11): 215-223.

• Graphics and Image Processing •

### Crop Segmentation Method of Remote Sensing Image Based on Improved DeepLabV3+ Network

REN Hongjie, LIU Ping, DAI Chao, SHI Juncai

1. College of Big Data, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
• Online:2022-06-01 Published:2022-06-01

### 改进DeepLabV3+网络的遥感影像农作物分割方法

1. 太原理工大学 大数据学院，山西 晋中 030600

Abstract: Aiming at the problems of low recognition accuracy, poor edge recognition effect and slow extraction speed in current remote sensing image crop extraction, a remote sensing image crop segmentation method based on improved DeepLabV3+ network is proposed. The feature extraction network is changed to a lighter MobileNetV2 network, and the ordinary convolution in the atrous spatial pyramid pooling module is changed to deep separable convolution, which greatly reduces the amount of model calculation and improves the calculation speed of the model. The double attention mechanism is added to the feature extraction module and the atrous spatial pyramid pooling module to further optimize the effect of model edge recognition and improve the accuracy of model segmentation. In addition, aiming at the imbalance of crop data sets, the weighted loss function is introduced to give different weights to corn, job’s tears and background classes, so as to improve the accuracy of crop region segmentation. Taking the UAV remote sensing image of an area in 2019 as the research object, corn and job’s tears are segmented. The experimental results show that the pixel accuracy of the improved DeepLabV3+ algorithm can reach 93.9%, the average recall can reach 90.7%, and the average intersection and merging ratio can reach 83.3%, which is better than the traditional segmentation methods commonly used for crop extraction, such as DeepLabV3+, Unet, Segnet and it has better segmentation effect on crops.