IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
  
  
  
   
  
  
   
    
      
     
    
    
    
    
    
    
    
    
     
    
       
   
  
   
    
CLASSIFICATION METHODS 
Dr. R Krishnan 
Director, ADRIN , Dept. of Space, 203, Akbar Road, Secundeabad. 
krishnan @adrin.res.in 
Commission VII, WGVII/1.2 
KEY WORDS: Supervised, Unsupervised, MLC, MLP, Sample, Adaptive, Classification NN, fuzzy. 
ABSTRACT: 
The process of classification can be considered as mapping of continuous data into categorical classes of information about the 
landscape. The tools of mapping can be either statistical, heuristic or a combination of both. This paper attempts to provide an 
overview of the current classification techniques in the context of increasing spatial and spectral resolution. 
INTRODUCTION 
Objective of Classification is to transform continuous data into 
categorical information classes describing the landscape, which 
can be used for decision making for effective management of 
natural resources. In the following sections an overview of the 
classification methods is provided. 
. 1. WHY CLASSIFICATION IS NEEDED? 
Classification is needed to convert a multitude of data into 
certain meaningful number of labels so that we can make sense 
of the environment from which the data has come. The methods 
used for classification depend on the types of classes that are 
sought to be identified, the resolution of the data (spectral & 
spatial), the need for crisp or fuzzy classes, separability 
between the classes and the knowledge about the distribution of 
the classes and the tolerable degree of penalty or loss associated 
with misclassification etc. 
2. WHATIS CLASSIFICATION? 
Classification in the context of remotely sensed data is to 
"link" each pixel in the image to one or more user defined 
labels, so that the radiometric information contained in the 
image is converted to thematic information, like vegetation, 
water, built up etc. "Link" is a mapping function which 
constructs a linkage between the raw data and user defined 
label set, Fig 1. 
Remote Sensin = : J fined 
Hu g Classifier User De fined 
magery Label Set 
0 Lg i 
   
  
Pasture 
E J T3 Water 
eer x ii NS sucer T] Bare Soil 
: = its [7] Forest 
Figure 1. Process of Classification [1] 
If the mapping function is a classification technique/algorithm 
through which each pixel is mapped to a single label, it is *one- 
to-one" mapping. Classifiers, which perform “one-to-one” 
mapping are called hard or crisp classifiers. 
It is also possible to perform "one-to-many" mapping; in this 
case, each pixel is associated with more than one label, with 
differing degrees of association between the pixel and each 
label and the degree of association is expressed as probabilities 
of membership. Classifiers that perform “one-to-many” 
mapping are called as soft classifiers. 
Before moving to classification techniques it is pertinent to 
mention about evolution of space borne sensor systems over the 
years. 
3. EVOLUTION OF SENSOR SYSTEMS 
Over the years there has been steady improvement in terms of 
spatial, spectral, temporal resolutions and data volumes, Fig 2, 
3 &A4. 
  
  
  
    
  
          
     
     
     
  
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Figure 2. Spatial resolution evolution [10] 
    
Devxaar resp 
Data Volume (MB) 
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