| Autor: |
Qiao Zhang, Ziyi Luo, Yang Shen, Zhoufeng Wang |
| Předmět: |
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| Zdroj: |
Photogrammetric Engineering & Remote Sensing; May2024, Vol. 90 Issue 5, p293-302, 10p |
| Abstrakt: |
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] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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