A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution

Timely and accurate delineation of the cropland extent over large area is crucial for operational agriculture monitoring and is also beneficial to address food security issues. Existing global datasets associated with cropland are limited by insufficient spatial resolution to properly represent areas with small parcel size distributions, and their less-than-ideal accuracies hamper application at regional and local scales. Diverse very high spatial resolution (VHSR) satellite systems are now available, offering sub-meter to five-meter resolution (e.g. Gaofen-1, Gaofen-2, and ZiYuan-3), and hence enabling explicit extraction of cropland areas from heterogeneous and fragmented landscapes. This study presented a generalized methodology for operational cropland mapping at very high resolution using a deep convolutional neural network to automatically learn the robust and discriminative features. Specifically, we slightly modified the pyramid scene parsing network (PSPNet) and combined deep long-range features with shadow local features to provide predictions with high level of detail. We demonstrated the modified PSPNet (MPSPNet) over four province-wide study areas (Heilongjiang, Hebei, Zhejiang and Guangdong) with diverse agrosystems across China from north to south using multi-source very high spatial resolution satellite images (mainly Gaofen-1 supplemented with Gaofen-2 and ZiYuan-3), with the overall accuracies ranging from 89.99% to 92.31%. Moreover, we compared MPSPNet with other CNN models and investigated the its behavior by visualizing the learned features on different layers, indicating that combining low and high level features for final classification was an efficient and accurate strategy for cropland mapping because the former capture edge information related to object boundaries and the latter could learn long-range spatial dependencies that helped recognize croplands. The temporal transfer and spatial transfer assessments from the respects of qualitative and quantitative corroborated the robust generalizability of the proposed method. And the contrast to the traditional object-based classification method also demonstrated the advantages and strong generalization capabilities of MPSPNet in extracting cropland using VHSR remote sensed images. We compared our results with the current cropland maps generated from FROM-GLC10, which further verified the effectiveness of the proposed approach for large-scale cropland mapping at very high resolution.