The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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or ecologically homogeneous land class. One reason that the 
object-based methods perform well in classifying high 
resolution images are because once the object is created by a 
segmentation approach, many more features such geometrical 
(e.g. shape and area) and topological properties (e.g. 
relationship between objects) can be extracted from the 
segmented image. This feature is particularly useful in 
classifying high spatial resolution images since high spatial 
resolution images often contain relatively fewer spectral bands 
(e.g. IKNONOS, QIUCKBIRD) compared to coarser images 
(e.g. MOIDS, Landsat TM). Consequently, methods that rely on 
only spectral information could have difficulty in distinguishing 
spectrally similar classes such as buildings and roads). Yet, it 
will be much easier to differentiate a building from a road if we 
can incorporate the object shape into the classification process. 
Hence, in this study, we propose to develop an integrated 
approach based on one-class SVMs and object-based methods 
to classify one land class from high spatial resolution images. 
We first segmented an image by a segmentation approach; both 
spectral and spatial properties were then extracted from objects, 
the one-class SVMs were then applied to extract one land type 
based on properties extracted from the objects. We also 
performed the comparisons among the proposed method and the 
one-class SVM with pixel-based classification. The overall 
accuracy and Kappa coefficient were calculated and used in the 
comparison (Congalton and Mead, 1983). 
2.2.2 Features extraction: After image segmentation, we then 
extract features to be used for image classification. One 
advantage of the object-based method is the ability to extract a 
wealth of features that could aid in classifying the imagery. 
Fourteen features are chosen in this study, detailed descriptions 
are described as follows: 
(1) Mean value (MV), which represents mean brightness value 
of every image object. Since the aerial photo includes three 
bands (i.e. green, blue, and red), we will have three mean 
values as features for image classification. The formula is as 
follows: 
¿*=2X ln 
;=i 
Where, L k is the mean brightness value; n is the number of 
pixels in the image object; B ik is brightness value of /th pixel 
contained in the image object in band k. 
(2) Mean difference to scene (MDS), which is the difference 
between mean brightness value of an image object and mean 
brightness value of the whole scene in band k. The formula is as 
follows: 
Sk = IX /n ' IX lm 
;=1 j=\ 
Where, S k is mean difference to the band k; m is the number of 
pixels of the whole scene. Similarly, there are three features 
which exist in the mean difference to the scene. 
2. DATA AND METHODS 
2.1 Data 
The high resolution remote sensing data used in this research 
are from aerial photos with 0.3 meter spatial resolution. The 
study area is located in Oakland, California (Figure 1). 
Figure 1. Aerial photograph of the study area (color image with 
0.3 meter spatial resolution) 
2.2 Methods 
(3)Mean difference to neighbour (MDN), which is the 
difference between mean brightness value of an image object 
and mean brightness value of its direct neighbours in band k. 
The formula is defined as follows: 
1=1 /=1 j—l /=1 
Where, p is the number of direct neighbours; mi is the number 
of pixels of the neighbour /. There are three MDNs. 
2.2.1 Segmentation method: Segmentation methods are used 
to generate image objects for classification and image retrieval, 
the object is defined as a group of spectrally similar contiguous 
pixels. Numerous algorithms have been proposed to segment an 
image. In this study, we used the segmentation method from 
Defmies software, which is based on a multi-resolution 
segmentation algorithm. The segmentation results are tuned 
based on scale parameters, color, smoothness, and compactness. 
The final segmentation results are shown in Figure 2. 
(4) Standard Deviation, which represents the standard deviation 
of brightness value of all the pixels contained in an image 
object in bank k. There are also three standard deviations. 
(5) Area, which represents the number of all the pixels contained 
in an image object. 
(6) Shape Index (SI) describes the smoothness of the image 
object borders, which is useful in differentiating houses and 
other man-made objects such as road. The definition of SI is: