A JOINT PIXEL AND REGION BASED MULTISCALE MARKOV RANDOM FIELD FOR
IMAGE CLASSIFICATION
Tiancan Mei* *, Lin Zheng? , Sidong Zhong*
a School of Electronic Information Wuhan University, Wuhan, China - (mtc, 00200535, zsd)@whu.edu.cn
Commission III, WG III/3
KEY WORDS: Image segmentation, Region based MRF, Pixel based MRF, High resolution image, Multiscale analysis, Watershed
transform
ABSTRACT:
MRF model is recognized as one of efficient tools for image classification. However, traditional MRF model prove to be limited for
high resolution image classification. This paper presents a joint pixel and region based multi-scale MRF model for high resolution
image classification. Based on initial image segmentation, the region shape information is integrated into MRF model to consider the
pixel and region information simultaneously. The region shaped information is used to complement spectral signature for alleviating
spectral signature ambiguity of different classes. The paper describes the unified multi-scale MRF model and classification algorithm.
The qualitative and quantitative comparison with traditional MRF model demonstrates that the proposed method can improve the
classification performance for regular shaped objects in high resolution image.
1. INTRODUCTION
Image classification is a process to categorize all pixels in an
image into one of several predefined classes. Each pixel is
assigned a class label based on the spectral signature of the
pixel and the relationship with its neighbors. The performance
of image classification is of paramount importance for many
subsequent image based applications. Information extraction
based on remote sensing image is an important application of
image classification. Once the classification result is obtained,
the categorized data can be used to produce thematic map of
land cover present in the image. With the availability of high
resolution remote sensing image, image classification has been
applied in several diversity applications, such as road network
extraction, urban planning and natural disasters prevention.
The classification of moderate spatial resolution remote sensing
image is mostly carried out based on the statistical separability
of each classes (Melgani F., et al, 2000). The performance of
pixel level classification is deteriorated when high resolution
image is under processed. The improvement of spatial
resolution increases the internal spectral variability within the
same land cover classes and decreases the spectral variability
between different classes. As a result, pixels will be
misclassified due to the ambiguity between different classes.
Despite the characteristic of high resolution image increase the
complexity of classification problem, it provides geometrical
information to be considered in the process of classification. So,
the critical part of high resolution image classification is how to
combine the spatial and spectral information to mitigate the
spectral ambiguity between different classes. Previous work on
high resolution image classification has been done by several
authors. Unsalan (Cen Uslan, and Kim L Boyer, 2004) proposed
a technique to identify different classes by using statistical
property of extracted straight lines and spatial coherence
constraint. Lee (Sanghoon Lee, Melba M. Crawford,
2005)presents a multi-stage image classification method. This
method first segments image by making use of spatial
* Corresponding author.
contextual information that characterizes the geophysical
connectedness of image structure. Then the segment results are
classified into distinct states by sequential region merging.
Bruzzone (L.Bruzzone, and L.Carlin, 2006)proposes a
supervised method to classify high resolution image. Adaptive
multilevel spatial context driven feature extraction is first
performed, and then the SVM classifier is used to obtain
classification results. In (L. Zhang, et al, 2006), a pixel shape
index is introduced to represent geometrical information and
SVM is used to classify the image. Z.Lei(Z.Lei, et al, 2011)
introduce the conditional texton forest to take use of spatial
contextual information to perform the land cover classification
for very high resolution image. Bellens(Rik Bellens, et al, 2008)
uses morphological profile to extract the geometrical
information as complement to spectral information.
Bouziani(Mourad Bouziani, et al, 2010)proposes a rule based
method for high resolution image classification by using
spectral, geometric, and contextual information.
The aforementioned previous work on high resolution image
classification shows that the combination of geometrical
information and spectral information are critical part of high
resolution image classification technique. Although great efforts
have been taken on this topic, few of the proposed methods can
meet the end-user requirement on accuracy and efficiency. For
this reason, there still remain many open issues that allow
further investigation.
Since the complexity of high resolution image classification,
besides the geometrical information, it is necessary to take into
account the contextual information about the image under
analysis. The Markov random field(MRF) is a powerful tool to
model the contextual information about the image. Bayesian
approach is a simple, yet effective tool to integrate both the
contextual information and statistical model of image in the
classification process. In fact, the Bayesian approaches have
been widely applied to perform image classification.
At the early stage of MRF used for image classification, it is
defined on the pixel level(Besag. J, 1986), because the
neighboring relationship among pixels is regular on the 2D