文献类型：会议论文

英文题名：FOREST LAND TYPE PRECISE CLASSIFICATIOIN BASED ON SPOT5 AND GF-1 IMAGES

作者：Ren, Chong[1] Ju, Hongbo[1] Zhang, Huaiqing[1] Huang, Jianwen[1]

通信作者：Ju, HB[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China

会议论文集：36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

会议日期：JUL 10-15, 2016

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：Multi-Source Data; GF-1 Image; Precise Classification; Hierarchical Information Extraction; Multiple Classifier Combination; Forest Land Type

年份：2016

摘要：The objective of this paper is to develop a hierarchical classification scheme and propose forest land type precise classification method based on SPOT5, GF-1 images, and other multi-source data, focusing on fine classification of forest land using high resolution remote sensing image in complex mountainous terrain conditions. The experiments were carried out by multi-source data integration, multiple features analysis and multiple classifier combination. The proposed method in this paper have advantages in fine identification of forest land types with high accuracy and high reliability, and the detail degree of fine identification reaches dominant tree species, which could fully meet the needs of forestry applications such as forest resources investigation, forest land change monitoring and thematic map digital update.