AGSPNet: A framework for parcel-scale crop fine-grained semantic change
  detection from UAV high-resolution imagery with agricultural geographic scene
  constraints
Shaochun Li Yanjun Wang Hengfan Cai Lina Deng Yunhao Lin
Real-time and accurate information on fine-grained changes in crop
cultivation is of great significance for crop growth monitoring, yield
prediction and agricultural structure adjustment. Aiming at the problems of
serious spectral confusion in visible high-resolution unmanned aerial vehicle
(UAV) images of different phases, interference of large complex background and
salt-and-pepper noise by existing semantic change detection (SCD) algorithms,
in order to effectively extract deep image features of crops and meet the
demand of agricultural practical engineering applications, this paper designs
and proposes an agricultural geographic scene and parcel-scale constrained SCD
framework for crops (AGSPNet). AGSPNet framework contains three parts:
agricultural geographic scene (AGS) division module, parcel edge extraction
module and crop SCD module. Meanwhile, we produce and introduce an UAV image
SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple
semantic variation types of crops in complex geographical scene. We conduct
comparative experiments and accuracy evaluations in two test areas of this
dataset, and the results show that the crop SCD results of AGSPNet consistently
outperform other deep learning SCD models in terms of quantity and quality,
with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining
improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over
the sub-optimal method. The method proposed in this paper can clearly detect
the fine-grained change information of crop types in complex scenes, which can
provide scientific and technical support for smart agriculture monitoring and
management, food policy formulation and food security assurance.