Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery
This addresses the need for generic land cover classification in applications like change detection and disaster monitoring, though it appears incremental as it builds on existing classification challenges.
The paper tackled the problem of spectral shift in land cover classification across diverse remote sensing images by developing a novel method for large-scale data from different areas and times, achieving outstanding classification accuracy compared to traditional methods.
Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset consisting of 150 Gaofen-2 imageries as data support for model training and performance evaluation. Our experiments achieve outstanding classification accuracy compared with traditional methods.