Study on Crop Planting Structure Extraction of Qingling River Irrigation Area in the Central Yunnan Plateau Based on Sentinel-2A Images
The high altitude of the Central Yunnan Plateau region results in fragmented crop distribution and small planting areas.Obtaining the crop planting structure quickly and accurately is of great significance for local agricultural irrigation and yield estimation.At present,there is little research on the complex crop areas in the central Yunnan Plateau based on Sentinel-2A image data.Therefore,a neural network,sup-port vector machine,and random forest classifier are constructed based on the combination of spectral,texture,and terrain features.The suit-able feature combination and optimal classifier for irrigation areas are analyzed and compared.The experimental results show that among the three classification models,support vector machines are more suitable for extracting planting structures in irrigation areas,with an overall ac-curacy of 91.74%and a Kappa coefficient of 0.87.On this basis,an object-oriented support vector machine model was constructed,and the overall accuracy of crop extraction was further improved,with an overall accuracy of 93.87%and a Kappa coefficient of 0.90.Compared with the traditional three feature combination support vector machine method,the overall accuracy was improved by 2.13%.The object-oriented support vector machine method is suitable for crop classification in the large-scale irrigation area of Qingling River in the central Yunnan Pla-teau,and can provide assistance for local water conservancy irrigation and agricultural economic development.
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