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IMAGE CLASSIFICATION WITH A REGION BASED APPROACH IN 
HIGH SPATIAL RESOLUTION IMAGERY 
Jong Yeol Lee *, Timothy A. Warner b 
? Korea Research Institute for Human Settlements, 1591-6, Kwanyang-Dong, Dongan-Gu, Anyang-Shi, Kyounggi- 
Do, 431-712 Republic of Korea - jylee(@kkrihs.re.kr 
" West Virginia University, Department of Geology and Geography, PO Box 6300, Morgantown, WV 26506-6300, 
USA - Tim. Warner(@Mail.wvu.edu 
  
KEYWORDS: Image segments, classification, object-oriented approach, multivariate analysis, high spatial 
resolution 
ABSTRACT: 
A number of studies have been carried out to find an appropriate spatial resolution to which to aggregate data in order 
to reduce the variation within an object, and minimize the classification error. Such approaches are pixel-based, and 
do not draw on the spatial variability as a source of information. The variability within an object can provide 
additional information that can be used for image classification. Instead of pixels, groups of pixels that form image 
segments, which are called “patches” in this study, were used for image classification. New methods that exploit 
multivariate statistics to improve the image classification are suggested. In the case of the object-based classification, 
patches are not expected to consist of pixels with completely homogeneous spectral radiances, but rather certain 
levels of variability are expected. To treat this variation within objects, multivariate normal distributions are assumed 
for every group of pixels in each patch, and multivariate variance-covariance matrices are calculated. A test of this 
approach was conducted using digital aerial imagery with a nominal one meter pixel size, and four multispectral 
bands, acquired over the small city of Morgantown, West Virginia, USA. Four classification methods were 
compared: the pixel-based ISODATA and maximum likelihood approaches, and region based maximum likelihood 
using patch means and patch probability density functions (pdfs). For region-based approaches, after initial 
segmentation, image patches were classified into seven classes: Building, Road, Forest, Lawn, Shadowed 
Vegetation, Water, and Shadow. Classification with ISODATA showed the lowest accuracy, a kappa index of 0.610. 
The highest accuracy, 0.783, was obtained from classification using the patch pdf. This classification also produced 
a visually pleasing product, with well-delineated objects and without the distracting salt-and-pepper effect of isolated 
misclassified pixels. The accuracies of classification with patch mean, and pixel based maximum likelihood were 
0.735, 0.687 respectively. 
single spatial resolution that suppresses all unwanted 
spectral variability (Marceau et al, 1994a; 1994b). 
Perfect classification could be achieved if each spectral Studies that use image segmentation to identify single 
1. INTRODUCTION 
class were to have a unique spectral signature. 
However, spectral overlap between most real classes 
occurs as a result of noise in the system, the natural 
variability of objects within a specific class, and the 
spatial variability of radiance within each object (Swain 
and Davis, 1978; Price, 1994). An added 
complication is that the spectral structure of an image is 
a function of scale (Cao and Lam, 1997). Higher 
spatial resolution may actually lead to greater 
variability within classes, as additional detail 1s 
resoived. 
A number of studies have been carried out to find an 
appropriate spatial resolution to which to aggregate 
data in order to reduce the variation within an object, 
and minimize the classification error (Pax-Lenney and 
Woodcock 1997; Teillet et al, 1997; Latty et al, 
1985). Such approaches are pixel-based, and do not 
draw on the spatial variability as a source of 
information. Another problem with methods that 
search for an optimal scale is that real objects and 
classes are variable in size, and thus there is usually no 
objects (Gougeon, 1995a) can overcome this problem 
ofa single optimal scale. However, most such studies 
use mainly aggregated information such as average DN, 
and to a limited extent the variance within the image 
segments (Kettig and Landgrebe, 1976; Gougeon, 
1995a; Meyer et al., 1996). The variability within an 
object can provide additional information that can be 
used for image classification. The spectral correlation 
between bands, as quantified by the covariance matrix, 
is in fact often a key determinant in traditional 
maximum likelihood classification for separating 
classes that overlap in their univariate distributions. 
2. TRADITIONAL IMAGE CLASSIFICATION 
Maximum likelihood classification is a standard, pixel- 
based supervised approach, which classifies unknown 
pixel-based on multivariate probability density 
functions (pdf) of the classes of interest. Statistical 
properties of training data sets from ground reference 
data are typically used to estimate the pdfs of the