Cropland Mapping in Fragmented Agricultural Landscape Using Modified Pyramid Scene Parsing Network

Abstract

It is significant to extract cropland mapping accurately and rapidly in many fields. In southern China, restrictions on complex planting structure and fragmented patches, long rainy season all pose challenges to remote sensing. Deep Learning has advantages in such complex classification problem. The combination of medium and high resolution data is an effective way to solve the problem of data acquisition and scale refinement in fragmented agricultural landscape area. In this paper, a modified Pyramid Scene Parsing Network which could integrate the medium-resolution data and the high-resolution data (HM_PSP) was proposed focusing on different scales features. The experimental results showed the applicability of the HM_PSP, which also had better visual results, the 3.42 % and 1.58% improvement of OA for the test sets. By comparing the results of HM_PSP and M_PSP (only trained by the medium resolution data), the enhanced effect after adding high-resolution data, as well as the generalization ability of spatial and temporal generalization ability of the model had been verified.