TY - GEN
T1 - The method study of extraction of tidal marsh vegetation based on object-oriented in Chongming Dongtan national nature reserve, Shanghai
AU - Zong, Wei
AU - Zhou, Yunxuan
AU - Lin, Wenpeng
AU - Zhou, Qiaolan
PY - 2010
Y1 - 2010
N2 - Chongming Dongtan national nature reserve is an internationally important wetland. Accurate survey of invasive alien vegetation and indigenous vegetation is the foundation of the study on the ecological impact of invasive vegetation, wetland protection and management. The high spatial resolution images have more spatial details and texture characteristics, and offer the possibility to extract information of tidal marsh vegetation. In this study, an object-oriented technology was applied to extract the information of tidal marsh vegetation from QuickBird image. Firstly, the image was segmented at two scales and objects were formed; secondly, the feature space was built using the characteristics of spectrum, shape and expert knowledge; and finally, a combining of near neighboring and fuzzy expert system was used to identify vegetation species. The results show that the classification accuracy for wetland reached 87.11%, higher than maximum likelihood classification by 18.75%.
AB - Chongming Dongtan national nature reserve is an internationally important wetland. Accurate survey of invasive alien vegetation and indigenous vegetation is the foundation of the study on the ecological impact of invasive vegetation, wetland protection and management. The high spatial resolution images have more spatial details and texture characteristics, and offer the possibility to extract information of tidal marsh vegetation. In this study, an object-oriented technology was applied to extract the information of tidal marsh vegetation from QuickBird image. Firstly, the image was segmented at two scales and objects were formed; secondly, the feature space was built using the characteristics of spectrum, shape and expert knowledge; and finally, a combining of near neighboring and fuzzy expert system was used to identify vegetation species. The results show that the classification accuracy for wetland reached 87.11%, higher than maximum likelihood classification by 18.75%.
KW - Invasive alien vegetation
KW - Object-oriente
KW - QuickBird
KW - Wetland
UR - https://www.scopus.com/pages/publications/78650573543
U2 - 10.1109/CISP.2010.5647883
DO - 10.1109/CISP.2010.5647883
M3 - 会议稿件
AN - SCOPUS:78650573543
SN - 9781424465149
T3 - Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
SP - 2265
EP - 2268
BT - Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
T2 - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Y2 - 16 October 2010 through 18 October 2010
ER -