首页|Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images
Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images
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NSTL
Elsevier
? 2021 Elsevier B.V.Extraction of cultivated land information from high spatial resolution remote sensing images is increasingly becoming an important approach to digitization and informatization in modern agriculture. The continuous development of deep learning technology has made it possible to extract information of cultivated land parcels by an intelligent way. Aiming at fine extraction of cultivated land parcels within large areas, this article builds a framework of geographical thematic scene division according to the rule of territorial differentiation in geography. A deep learning semantic segmentation network, improved U-net with depthwise separable convolution (DSCUnet), is proposed to achieve the division of the whole image. Then, an extended multichannel richer convolutional features (RCF) network is involved to delineate the boundaries of cultivated land parcels from agricultural functional scenes obtained by the former step. In order to testify the feasibility and effectiveness of the proposed methods, this article implemented experiments using Gaofen-2 images with different spatial resolution. The results show an outstanding performance using methods proposed in this article in both dividing agricultural functional scenes and delineating cultivated land parcels compared with other commonly used methods. Meanwhile, the extraction results have the highest accuracy in both the traditional evaluation indices (like Precision, Recall, F1, and IoU) and geometric boundary precision of cultivated land parcels. The methods in this article can provide a feasible solution to the problem of finely extracting cultivated land parcels information within large areas and complex landscape conditions in practical applications.
Agricultural remote sensingCultivated land parcelsDeep learningGeographical Thematic scenesSemantic segmentation
Dong D.、Zhou C.、Xu L.、Ming D.、Du T.、Chen Y.
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School of Information Engineering China University of Geosciences (Beijing)
State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences
NSTL
Elsevier
