IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
  
MULTI-RESOLUTION SEGMENTATION AND CLASSIFICATION OF 
REMOTE SENSING DATA 
P.V.Narasimha Rao', K. Dhanunjaya Reddy*, T.Sanjeev Kumar* and B.R.M.Rao' 
* A&S Group, National Remote Sensing Agency, Balanagar, Hyderabad, Andhra Pradesh, India — 500 037 
email: rao_pvn@nrsa.gov.in, rao_brm@nrsa.gov.in 
*ND Soft Spatial Systems Pvt. Ltd., Panjagutta Colony, Hyderabad-16, Andhra Pradesh, India, email:dreddy @ndsss.com 
KEY WORDS: Remote sensing, Multi-resolution segmentation, Classification, Image objects, Image texture 
ABSTRACT: 
Classification of moderate to coarse resolution remote sensing data into land use and land cover classes by per pixel classifiers is 
known for decades. Availability of high resolution data from space platforms in recent years necessitated alternative approaches in 
digital classification to satisfactorily account for inter and intra class variability of such data. Among several possible alternatives, 
object oriented multi-resolution segmentation and classification approach, commercially known as eCognition, has shown greater 
potential. It has flexibility to use relations across different segmentation levels and features within a segmentation level to realize 
better classification accuracy. In the present study, object oriented multi-resolution segmentation and classification approach has 
been applied to the 24m resolution IRS-1D LISS-III data of two test sites with a wide range of heterogeneity in land use and land 
covers. Performance of the adopted approach is superior to that of per pixel classifier. The approach provided the options of (i) 
interactive as well as automatic classifications, (ii) using class hierarchy and contextual information, and (iii) provides information at 
different resolutions and scales. However, multi-resolution segmentation is based on certain heuristics and data dependent. Sufficient 
care needs to be exercised during image segmentation to realize acceptable accuracy of classification. 
1. INTRODUCTION 
Segmentation is the subdivision of an image into separated 
regions. Grouping of pixels in a remote sensing image (or 
classification) into meaningful land use and land cover classes 
using per pixel classifiers is known for decades. While per pixel 
classifiers by supervised and unsupervised techniques yield 
satisfactory results with medium to coarse resolution datasets, 
their performance in general, is inadequate with highly textured 
radar data and high resolution optical data. Increased 
availability of high resolution remote sensing satellite data from 
such sensors as IRS-LISS-III and IKONOS in recent years has 
brought out the inadequacies of the per pixel classifiers to 
account for the variability associated with the natural features. 
The assumption of the per pixel classifiers that pixels are 
statistically independent is unrealistic as neighbouring pixels 
are more likely to be similar. The per pixel classifiers do not 
take into account the relationship between the scale of objects 
being classified and the size of the image pixel. They do not use 
two of the key features used by the human visual recognition 
system, image texture and context. Patterns are built up from 
below on a pixel by pixel basis on the assumption that each 
pixel is separate entity. Texture which is a measure of the local 
variation in pixel values and context, the logical relationship 
between an object and its neighbours, are ignored. (Mather, 
P.M., 1990). In addition, need for extraction of information 
residing at different spatial resolutions and scales is appreciated 
more in recent times. As a result, alternative methods, including 
texture and contextual information, information fusion, 
hierarchical classification and object oriented multi-resolution 
segmentation and classification of remote sensing data have 
come into prominence. 
Some of the simplest approaches to segmentation are global 
thresholding, region growing by clustering, texture 
segmentation algorithms by co-occurrence matrices (Haralick et 
al. 1963), MRF models (Mao and Jain, 1992), wavelet 
coefficients (Salari and Zing, 1995), fractal indices (Chaudhuri 
and Sarkar, 1995) and knowledge based approaches (Gorte, 
    
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1998). More recently, Definiens Imaging, Germany developed 
eCognition™, an object oriented multi-resolution segmentation 
and classification scheme that extracts homogeneous image 
objects and their relations. 
In the present study the eCognition software has been used with 
the main objectives of investigating the suitability of object 
based segmentation and classification of remote sensing data 
for land use and land cover. 
1.1 Object oriented multi-resolution segmentation and 
classification: 
The adopted technique, which is commercially available by 
name eCognition'“ is based on the concept that important 
semantic information necessary to interpret an image is not 
represented in single pixels but in meaningful image objects 
and their mutual relations. The basic difference of the adopted 
procedure with the per pixel based classifiers is that it does not 
classify single pixel but rather image objects formed by a group 
of pixels during the segmentation step. In the first step, the 
software extracts homogeneous image objects in any chosen 
resolution that are subsequently classified by means of fuzzy 
logic. The basic strategy is to build up a hierarchical network of 
image objects, which allows the representation of the image 
information content at different resolutions (scales) 
simultaneously. By operating on the relations between 
networked objects, it is possible to classify local context 
information. Beyond the pure spectral information this "context 
information" (which often is essential) can be used together 
with form and texture features of image objects to improve 
classification. Multi-resolution segmentation separates adjacent 
regions in an image as long as they are significantly contrasting 
to each other and generates homogeneous regions. In addition, 
a hierarchical network of image objects allows the 
representation of image information in different resolutions 
simultaneously, allowing classification of relationships at 
different scales. As the image objects represent meaningful 
areas the networking of adjacent and hierarchically grouped 
    
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