会议论文

Chen, Li

英文

2014 THIRD INTERNATIONAL WORKSHOP ON EARTH OBSERVATION AND REMOTE SENSING APPLICATIONS (EORSA 2014)

2380-8039

2014

3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

International Workshop on Earth Observation and Remote Sensing Applications

JUN 11-14, 2014

Changsha, PEOPLES R CHINA

[Chen, Li;Lin, Hui;Wang, Guangxing;Sun, Hua;Yan, Enping] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.^[Wang, Guangxing] So Illinois Univ, Dept Geog, Carbondale, IL 62901 USA.

Cent S Univ Forestry & Technol Univ, Cent S Univ, Hunan Univ Sci & Technol, Indiana State Univ, IEEE Geoscience & Remote Sensing Soc, Int Soc Photogrammetry & Remote Sensing, Grp Earth Observat, Nat Sci Fdn China

Weng, Q Gamba, P Xian, G Wang, G Zhu, J

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-4799-4184-1

本校为第一且通讯机构

Because of high spectral and temporal resolutions, large coverage and low cost, MODIS (Moderate Resolution Imaging Spectroradiometer) data has been widely used to extract information of forest types at regional, national and global scales. However, its coarse spatial resolution often leads to mixed pixels and impedes increasing classification accuracy of forest types. Spectral unmixing can, to some extent, increase the accuracy of classification. But, how to accurately extract pure endmembers for a study area is a great challenge. The selection of linear or non-linear spectral unmixing algorit...