FULLY CONVOLUTIONAL NETWORK BASED SHADOW EXTRACTION FROM GF-2 IMAGERY
There are many shadows on the high spatial resolution satellite images,especially in the urban areas.Although shadows on imagery severely affect the information extraction of land cover or land use,they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself.This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city.By means of spatial filtering and calculation of adjacent relationship along the sunlight direction,the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows.Finally,the building shadows were separated.The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms.It showed that the deep learning network approach can improve the accuracy to a large extent.
Fully Convolutional Network Shadow Extraction Deep Learning GF-2 Building Shadows
LI Zhi-qiang CAI Guo-yin Ren Hui-qun
School of Geomatics and Urban Spatial Informatics,Beijing University of Civil Engineering and Architecture,Beijing,China;The Key Laboratory for Urban Geomatics of National Administration of Surveying,Mapping and Geoinformation,Beijing University of Civil Engineeringand Architecture,Beijing,100044,China
国际会议
北京
英文
985-991
2018-05-07（万方平台首次上网日期，不代表论文的发表时间）