Sugarcane Planting Area Classification, Extraction and Accuracy Comparison Based on Chinese High-Resolution Remote Sensing Satellite Data: A Case Study of Ningming Sugarcane Demonstration Area, Chongzuo City, Guangxi

Abstract

Abstract Remote sensing techniques are effective in sugarcane extraction and monitoring, but most of the existing research is based on low- and medium-resolution image. Thus, the technical methodology for high-resolution image needs to be improved. Due to the good performances of deep learning algorithms in solving classification problems for the very high resolution (VHR) images, the target mask U-Net model is introduced to research VHR satellite data from China, i. e., the GaoFen-1 (GF-1), GaoFen-2 (GF-2) and ZiYuan-3 (ZY-3). First, a sugarcane area was classified and extracted in the Ningming Sugarcane Demonstration Area in Chongzuo City, Guangxi. Further, we validated and compared the extraction accuracies for different satellite data. The results showed that the extraction accuracies of the GF-1, GF-2 and ZY-3 were 79.97% (Kappa coefficient of 0.19), 94.02% (Kappa coefficient of 0.82) and 81.94% (Kappa coefficient of 0.35), respectively. The spectral and textural information of high-resolution images can effectively guarantee improvements to the accuracy of crop extraction. By comparison of data sources and traditional supervision classification methods, the GF-2 data features the best results for sugarcane extraction. The technical methods and experimental results in this paper not only confirm the feasibility of applying China’s VHR data to monitor sugarcane planting areas, but also provides reference for the relevant future studies.