Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery

Autor:	Tong, Xin-Yi, Lu, Qikai, Xia, Gui-Song, Zhang, Liangpei
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: IGARSS'18 conference paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1806.00901 Zobrazit plný text záznamu View this record from Arxiv