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Real-time and accurate information on fine-grained changes in cropcultivation is of great significance for crop growth monitoring, yieldprediction and agricultural structure adjustment. Aiming at the problems ofserious spectral confusion in visible high-resolution unmanned aerial vehicle(UAV) images of different phases, interference of large complex background andsalt-and-pepper noise by existing semantic change detection (SCD) algorithms,in order to effectively extract deep image features of crops and meet thedemand of agricultural practical engineering applications, this paper designsand proposes an agricultural geographic scene and parcel-scale constrained SCDframework for crops (AGSPNet). AGSPNet framework contains three parts:agricultural geographic scene (AGS) division module, parcel edge extractionmodule and crop SCD module. Meanwhile, we produce and introduce an UAV imageSCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiplesemantic variation types of crops in complex geographical scene. We conductcomparative experiments and accuracy evaluations in two test areas of thisdataset, and the results show that the crop SCD results of AGSPNet consistentlyoutperform other deep learning SCD models in terms of quantity and quality,with the evaluation metrics F1-score, kappa, OA, and mIoU obtainingimprovements of 0.038, 0.021, 0.011 and 0.062, respectively, on average overthe sub-optimal method. The method proposed in this paper can clearly detectthe fine-grained change information of crop types in complex scenes, which canprovide scientific and technical support for smart agriculture monitoring andmanagement, food policy formulation and food security assurance.

AGSPNet: A framework for parcel-scale crop fine-grained semantic change  detection from UAV high-resolution imagery with agricultural geographic scene  constraints