An Object-Oriented Classification Method on High
Resolution Satellite Data
Sun Xiaoxia, Zhang Jixian, Liu Zhengjun
Chinese Academy of Surveying and Mapping, No16,
Beitaiping Rd, Haidian District ,Beijing,
100039, China
Email: sun.xiaoxia@163.com
To traditional moderate or low resolution satellite data, the data processing or
information detecting is only on a per-pixel basis because of the impacts to geometric accuracy
of spatial resolution, Thereby only the spectral information is used for the classification. High
spatial resolution sensors involves a general increase of spatial information and the accuracy of
results may decrease on a per-pixel basis. In order to realise the full potential of the VHR image
data, An object-oriented image analysis is implemented with the software eCognition. It is
based on fuzzy logic, allows the integration of different object featrues, such as spectral values,
shape and texture. In this paper we analysis an object-oriented classification method using
QuickBird panchromatic and multispectral data on the test area of the PuDong New district of
ShangHai.The analysis includes two parts: first dividing the image data into segments and then
classifying the segments by means of fuzzy approach of nearest neighbour classifier.
1. INTRODUCTION
With the development of satellite technologies,some very high resolution (VHR) satellite data
whose metric characteristics are acceptable for large scale land use mapping has been produced.
Traditional supervised classification and unsupervised classification are based on the grey value
of pixel itself, that is to say, only the spectral information is used for classification. The result
will be unacceptable when classifying the VHR image data. To solve this problem an
object-oriented classification method utilizing image segmentation and fuzzy classification on
the results of segmentation is suggested.
In this method, the processing units are no longer single pixels but image objects. Firstly, the
complete image has to be segmented into meaning pixel groups ,namely segments. Secondly, A
set of knowledge-based classification rules to describe each class should be define. The rules
includes spectral, spatial, contextual, and textual information. And then, classifier will be
choosen to assign each segment to the proper class according to the rules(Leukert,2004).
Compared to conventional pixel-based classification approaches,utilizing only the spectral
response, image objects contain additional information, like object texture,shape, relations to
adjacent regions.The results of classification are advaced from pixel-basis to object-basis and
the accuracy of auto recognition on VHR data is improved largely. This approach can validly
satisfy the large scale land use mapping and investigations.
This paper starts with a short introduction of the object-based classification method. After
pre-processing of Quickbird image data by EARDAS software, the practical segmentation and
classification are implemented by eCognition, and at the last, a summary and a concluson is
given.
