Refined identification and classification of crop planting structure at plot scale based on Segformer network
Preventing the"non-grain"of cultivated land and stabilizing food production are the cornerstones of Chi-na's food security.In order to realize the fine identification and classification of crop types and planting structure in the are-a of land fragmentation,this study took Taixing City,Jiangsu province as the research area,and realized the fine extraction of cultivated land information at the plot scale based on the high-resolution remote sensing images and the Segformer seman-tic segmentation model with significant multi-spatial scale fusion features.At the same time,the normalized difference vege-tation index(NDVI)time series curve of the main vegetation types and the spectral reflectance characteristics at the key time nodes of vegetation growth were constructed by combining multi-source remote sensing data,and the classification of crop planting structure at the plot scale was carried out.The results showed that the segmentation method based on Segformer model could effectively identify cultivated land,and the F1 was 92.4%.The classification method of crop planting structure based on multi-temporal NDVI time series characteristics of main vegetation types and spectral reflection characteristics at key time nodes of vegetation growth could realize the classification of planting structure at plot scale,and the overall classification accuracy was 82.38%.Therefore,the method established in this study could effectively realize the fine extraction of cultivated land information at the plot scale and the identification and classification of planting structure,and provide technical support for cultivated land protection.
planting systemplot scalerefined identification and classificationremote sensing
万方数据
维普
