1. Parcel-Level Crop Classification in Plain Fragmented Regions Based on Multi-Source Remote Sensing Images.
- Author
-
Qiao Zhang, Ziyi Luo, Yang Shen, and Zhoufeng Wang
- Subjects
SUPPORT vector machines ,CROPS ,DEEP learning ,CLASSIFICATION ,ARABLE land - Abstract
Accurately obtaining crop cultivation extent and estimating the cultivated area are significant for adjusting regional planting structure. This study proposes a parcel-level crop classification method using time-series, medium-resolution, remote sensing images and singlephase, high-spatial-resolution, remote sensing images. The deep learning semantic segmentation network feature pyramid network with squeeze-and-excitation network (FPN-SENet) and multi-scale segmentation were used to extract cultivated land parcels from Gaofen-2 imagery, while the pixel-level crop types were classified by using support vector machine algorithms from time-series Sentinel-2 images. Then, the parcel-level crop classification was obtained from the pixel-level crop types and land parcels. The proposed method was tested in southwestern China to extract main winter-spring crops and achieved a good performance. Specifically, the FPN-SENet model outperformed other models in cultivated land extraction, with an F1 of 0.872. The crop classification overall accuracy is 0.910 and the kappa coefficient is 0.861. This study provides a technical reference for monitoring cultivated land and can be applied in other regions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF