Key technologies of land use information extraction based upon multisource remote sensing data - A case study of hilly-plain transition region in the middle and lower reaches of the Yellow River

被引:1
|
作者
Lu, Heli [1 ]
Liu, Guifang [1 ]
机构
[1] Henan Univ, Inst Nat Resources & Environm Sci, Kaifeng 475004, Peoples R China
关键词
multi-source remote sensing data; land use information extraction; multi-level supervised classification; expert knowledge database; CLASSIFICATION;
D O I
10.1117/12.869066
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
It was a trend that image was classified through combining multi-source remote sensing data with non-remote sensing data by GIS technology. In this paper, technological framework of land use information extraction was established using multi-sources remote sensing data (TM and CBERS-02B), DEM, slope data, land use map and other geographic auxiliary data. The result showed :(1) It was possible to combine TM and CBERS-02B as land use sources data because of their similar spatial resolution and spectral resolution. In this research the method of multi-level supervised classification was adopted. (2) Interpretation accuracy was improved by establishing background database through GIS technology. First, non-remote sensing information, such as topographic map, soil map, land-use map, transportation map, etc, was integrated as background database. Then land use classifications were overlapped with above database. The results showed that uncertainty could reduce by 23.2%. (3) In the study area dry land spectrums in plain area, hilly area and the Yellow River flood plain were very different and spectrums of habitation in plain and the Yellow River land wash were the same. As for above phenomenon of "same object with different spectrums" and "different objects with same spectrum", expert knowledge database was established based upon relationship between remote sensing image and geographical environment. As a result average classification accuracy was improved by 12.1%.
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页数:8
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