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Mapping without the sun
Zhang, Jixian

GU Haiyan a,b ’ *, LI Haitao 3 , ZHANG feng c , HAN Yanshun 3 , YANG Jinghui 3
a Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, 100039,
b Institute of Surveying and Geography Science, Liaoning Technical University, Liao Ning, 123000, China.
c Civil Engineering Department, Guilin University of Technology, Gui Lin, 541000, China.
Commission VI, WG VI/4
KEY WORDS: Object-oriented Classification, Land-cover Classification, Markov Random Field (MRF), Support Vector Machine
(SVM), QuickBird
We present a new object-oriented land cover classification method integrating raster analysis and vector analysis, which adopted
Markov Random Field (MRF) for segmentation and Support Vector Machine (SVM) for classification using High Resolution (HR)
QuickBird data. It synthesized the advantage of digital image processing, Geographical Information System (GIS) (vector-based
feature selection) and Data Mining (intelligent SVM classification) to interpret image from pixels to segments and then to thematic
information. Compared with the pixel-based SVM classification in Imagelnfo 3.0, both of the accuracy of land cover classification
by the proposed method and the computational performance for classification were improved. Moreover, the land cover
classification map can update GIS database in a quick and convenient way.
For years, many efforts have been made to develop automatic
procedures for integrating raster analysis and vector analysis.
Numerous researches have been focusing on updating GIS
databases using remote sensing data and land cover
classification technique. The High Resolution (HR) imagery has
the advantage of high user interpretability, rich information
content, sharpness, high image clarity, and integrity, which
provides the unique tool for classification. However, it is not
practical to classify the HR imagery using traditional pixel-
based classification methods (e.g., Maximum Likelihood,
ISODATA, K-means). Recently, researchers have explored new
classification techniques, such as multi-classifier systems that
integrate the outputs of several underlying classifier algorithms,
and the use of structural, spatial, context information from
imagery. However, these pixel-based classification techniques
improve classification accuracy with some limitations: 1) they
have considerable difficulties dealing with the abundant
information of HR data, 2) they produce a characteristic and
inconsistent salt-and-pepper classification, 3) they are not
capable of extracting objects of interest, and 4) they can not
update GIS database expediently (Baatz, M., et al., 2004). Thus,
the pixel-based classification methods are no longer applicable
for HR imagery.
In this situation, commercial software, eCognition, brought
forward the revolutionary of object-oriented approach, which
adopts a bottom-up region-merging segmentation technique and
fuzzy logical classification method.Recently, some researchers
have been succeeded in classification with new object-oriented
classification methods adopting other segmentation and
classification techniques. Furthermore, in object-oriented image
analysis, multi-source data fusion technique is helpful to land
cover classification. Cuozzo,G.(2004) succeeded in
classification with new object-oriented classification methods
adopting Markov Random Field (MRF) segmentation
techniques and MLC algorithm. Bruzzone, L.(2004) proposed a
multi-level hierarchical approach to classify high spatial
resolution images with Support Vector Machines (SVM), and
the results and comparisons confirmed the effectiveness of the
approach. Li, H.T. (2007) succeeded in object-oriented
classification based on improved Color Structure Code (CSC)
and SVM. In this paper, an object-oriented classification
scheme based on MRF and SVM is developed where QuickBird
imageries are used.
2.1 MRF Segmentation Method
Markov Random Fields (MRF) are commonly used
probabilistic models for image analysis. The basic idea of MRF
is to model the contextual correlation among image pixels in
terms of conditional prior probabilities of individual pixels
given their neighboring pixels.
The segmentation is obtained as a cartoon image, which is
basically a labeling of the original image (Kato, Z., et al, 2006).
The original image is showed as:
F = {/ = 0',/);l < i < m, 1 < j < n) , (1)
The segmentation imageX = {x ? ;x s e {1,2,...,AT}} is a random
field defined in F . X s is the label of point S . K is the numbers
*E-mail: haiyanl982709@126.com; Phone : 86 10 88217730; Fax: 86 10 68211420; http:// www.casm.ac.cn