Research on Large Area and Plot Level Crop Classification Methods Based on Multi-model Combination
Crop fine classification information is the basic data of crop monitoring. With the rapid development of remote sensing tech-nology in the theory,technique and method of crop classification,intelligent crop recognition based on multi-source and multi-time series remote sensing images has been applied more and more widely. For large-scale and plot-level crop planting structure investiga-tion,remote sensing technology is still unable to fully support the engineering application of crop investigation due to the lack of train-ing samples,lack of complete agricultural investigation statistical system and other factors. In view of the current practical require-ments of smart agriculture and precision agriculture management,this paper proposed a "multi-model" combination investigation method of large-scale and plot-level crop planting structure based on intelligent interpretation of high-resolution remote sensing ima-ges,field investigation and manual editing by using "agricultural thematic data"as basic quantification and took Qiqihar city as an ex-ample for application verification. Confusion matrix was used to verify the accuracy of crop classification results. The overall accuracy and Kappa coefficient were 94.66％ and 0.91,respectively,indicating high accuracy of classification results. Through verification,this method can accurately obtain the spatial data of crop classification at plot-level,which can provide accurate data support for pre-cision agriculture and smart agriculture management.
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