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Farmland Segmentation from Remote Sensing Images Using Deep Learning Methods

文献类型： 会议论文

第一作者： Wei Sun

作者： Wei Sun 1 ; Rong Zhou 2 ; Congchong Nie 3 ; Liuan Wang 2 ; Jun Sun 2 ;

作者机构： 1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China

2.Fujitsu R&D Center Co., Ltd., Beijing, China

3.State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing,Wuhan University, Wuhan, China

关键词： Farmland segmentation;remote sensing;satellite imagery;deep semantic segmentation;agriculture insurance

会议名称： Conference on remote sensing for agriculture, ecosystems, and hydrology

主办单位：

页码： 1152809.1-1152809.7

摘要： Farmland segmentation is crucial and getting increasingly important role in the field of agricultural insurance and digital agriculture. To accurately achieve insured crop area and disaster loss assessment, precision smallholders' farmland segmentation and mapping are necessary. Deep learning technology has demonstrated its strength and has out-performed state-of-the-art alternatives in many fields. In this study, we aim to explore the effectiveness of five classic deep semantic segmentation models on the smallholder-wise farmland segmentation from remote sensing images. Five FCN-based segmentation models were selected including U-Net, PSPNet. DeepLabV3+. DANet. and CCNet, which were originally proposed for natural or medical image segmentation. The study area locates in Jiaxiang County in the north China. We used GF-1 at 2m spatial resolution with 4-band multispectral images (red. green, blue, and near-infrared) acquired based on image fusion. Comparative experiment results showed that DeepLabv3+ achieved the best mloU of 89.82%. PSPNet, DANet. CCNet and U-Net obtained lower but similar mloU with: 89.63%, 89.66%, 89.59%. and 89.15%. The OA metric of the all the five models were above 94.8%. The results indicate that the FCN-based deep semantic segmentation networks are effective for larger-scale smallholder-wise farmland segmentation with high spatial resolution multispectral satellite images.

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