In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 90, No. 5 ( 2024-05-01), p. 293-302
Abstract:
Accurately obtaining crop cultivation extent and estimating the cultivated area are significant for adjusting regional planting structure. This article proposes a parcel-level crop classification method using time-series, medium-resolution, remote sensing images and single-phase, 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.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.23-00053R2
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2024
detail.hit.zdb_id:
2317128-5
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