关键词: 遥感, 作物分类, 多时相, GF-1, 决策树

Abstract: GF-1/WFV remote sensing image has higher temporal and spatial resolution. It has obvious advantages to carry out crop classification survey using multiphase images. This paper took Yingshang county of Anhui Province as the study area and extracted main crop classification based on the GF-1/WFV satellite remote sensing image data from May to September 2017. Firstly, through analyzing the sequential variation characteristics of major crops typical vegetation index NDVI, EVI and WDRVI in the study area, made clear the response characteristics of different crops in each time. Then, based on sensitive VI changes of crops in different time phases, the paper constructed decision tree classification model, and successfully extracted the spatial distribution of 4 major crops: corn, rice, soybean and sweet potato in the study area. The results showed that the overall accuracy was 90.9%, and Kappa coefficient was 0.895. At the same time, this paper used the maximum likelihood method, supported vector machine (SVM) for crop classification. The comparison results showed that the maximum likelihood method was the worst, and the support vector machine was second, and the decision tree classification method was the best. Above results indicated that using the remote sensing image data of time series of multi-phase combined with the feature of crop vegetation index, the classification method of decision tree could effectively improve the accuracy of crop classification.

Key words: remote sensing, crop classification, multidate, GF-1, decision tree