Details
| Original language | English |
|---|---|
| Title of host publication | 2009 Joint Urban Remote Sensing Event |
| Publisher | IEEE Computer Society |
| ISBN (print) | 9781424434619 |
| Publication status | Published - 2009 |
| Event | 2009 Joint Urban Remote Sensing Event - Shanghai, China Duration: 20 May 2009 → 22 May 2009 |
Abstract
The advent of advanced processing techniques and high speed computers have led to the possibility of supplementary hyperspectral data with information about different kinds of object features that can be observed in the images, for example, shape and size. Other data sources, e.g., digital surface model from airborne laser scanning data, can provide height information for the object features. In this paper an improved binary encoding method (IBE) is proposed to integrate such additional information into the binary encoding matching method. The original binary encoding method proceeded spectral information pixel by pixel; IBE method is based on object-based classification. The hyperspectral and DSM data were corporately used in the method. During the method, the information of target objects was represented by 280 binary codes according to IBE rules, practical experiences and user requirements. We applied the proposed method to classify the test area. The results show that the proposed method needs less training data, lower computation cost and can gain higher classification accuracy. It is beneficial especially for limited spatial extent and great variation of the ground contents.
Keywords
- Binary encoding, DSM, Hyperspectral, Object-based classification
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Networks and Communications
- Environmental Science(all)
- Ecology
- Environmental Science(all)
- Nature and Landscape Conservation
Cite this
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2009 Joint Urban Remote Sensing Event. IEEE Computer Society, 2009. 5137551.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Object-based binary encoding algorithm -an integration of hyperspectral data and DSM
AU - Huan, Xie
AU - Xiaohua, Tong
AU - Heipke, Christian
AU - Lohmann, Peter
AU - Sörgel, Uwe
PY - 2009
Y1 - 2009
N2 - The advent of advanced processing techniques and high speed computers have led to the possibility of supplementary hyperspectral data with information about different kinds of object features that can be observed in the images, for example, shape and size. Other data sources, e.g., digital surface model from airborne laser scanning data, can provide height information for the object features. In this paper an improved binary encoding method (IBE) is proposed to integrate such additional information into the binary encoding matching method. The original binary encoding method proceeded spectral information pixel by pixel; IBE method is based on object-based classification. The hyperspectral and DSM data were corporately used in the method. During the method, the information of target objects was represented by 280 binary codes according to IBE rules, practical experiences and user requirements. We applied the proposed method to classify the test area. The results show that the proposed method needs less training data, lower computation cost and can gain higher classification accuracy. It is beneficial especially for limited spatial extent and great variation of the ground contents.
AB - The advent of advanced processing techniques and high speed computers have led to the possibility of supplementary hyperspectral data with information about different kinds of object features that can be observed in the images, for example, shape and size. Other data sources, e.g., digital surface model from airborne laser scanning data, can provide height information for the object features. In this paper an improved binary encoding method (IBE) is proposed to integrate such additional information into the binary encoding matching method. The original binary encoding method proceeded spectral information pixel by pixel; IBE method is based on object-based classification. The hyperspectral and DSM data were corporately used in the method. During the method, the information of target objects was represented by 280 binary codes according to IBE rules, practical experiences and user requirements. We applied the proposed method to classify the test area. The results show that the proposed method needs less training data, lower computation cost and can gain higher classification accuracy. It is beneficial especially for limited spatial extent and great variation of the ground contents.
KW - Binary encoding
KW - DSM
KW - Hyperspectral
KW - Object-based classification
UR - http://www.scopus.com/inward/record.url?scp=70350140326&partnerID=8YFLogxK
U2 - 10.1109/URS.2009.5137551
DO - 10.1109/URS.2009.5137551
M3 - Conference contribution
AN - SCOPUS:70350140326
SN - 9781424434619
BT - 2009 Joint Urban Remote Sensing Event
PB - IEEE Computer Society
T2 - 2009 Joint Urban Remote Sensing Event
Y2 - 20 May 2009 through 22 May 2009
ER -