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IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
AN OVERVIEW OF FUZZY METHODS FOR LAND COVER CLASSIFICATION FROM 
REMOTE SENSING IMAGES 
M. A. Ibrahim, M. K. Arora!, S. K. Ghosh and A. M. Chandra 
Department of Civil Engineering, I. I. T. Roorkee, ROORKEE 247667 
KEY WORDS: Image classification, fuzzy methods, mixed pixels, accuracy assessment, fully fuzzy classification, land cover, 
remote sensing. 
ABSTRACT 
During the last decade there has been resurgency in the application of fuzzy methods for the classification of remote sensing images, 
which are dominated by mixed pixels. These methods are used to unmix the classes within the mixed pixels or in other words 
perform sub-pixel classification. The methods appear to be more suitable in the Indian conditions where the areas are largely 
dominated by mixture of classes. In this paper, an overview of some prevalent fuzzy classification methods and the accuracy 
measures specifically designed to evaluate the performance of fuzzy classifications is presented. 
1. INTRODUCTION 
Often, remote sensing images are dominated by mixed pixels, 
which do not represent a single land cover class but contain two 
or more classes. For instance, In India, within a small stretch, 
one may find forestland, agricultural land, residential areas and 
water bodies. As a result, the land cover classes are generally 
mixed in nature and inter-grade gradually in an area (Foody and 
Cox, 1994). The mixed pixels also occur at the boundaries of 
two land cover classes. Furthermore, the mapping from remote 
sensing is generally carried out at regional and global scales, 
which requires coarse spatial resolution images where the 
chances of occurrence of mixed pixels are high. Error is likely 
to occur in the classification of image dominated by mixed 
pixels. The conventional use of crisp classification methods 
such as maximum likelihood classification (MLC) that allocates 
one class to a pixel may tend to over- and underestimate the 
actual areal extents of the classes on ground and thus may 
provide erroneous results. A range of alternative methods such 
as Linear Mixture Modeling (LMM), Fuzzy- c Means (FCM) 
algorithm, Artificial Neural Network (ANN) and Knowledge 
Based (KB) approaches may be applied. Foody et al. (1992) 
showed that MLC could also be employed as a fuzzy 
classification method. Recently, a newer classification method 
namely support vector machines (Brown et al., 2000) has also 
been applied to unmix the classes within a pixel. 
In essence, fuzzy methods tend to resolve a pixel into various 
class components, thus generating fuzzy class outputs in the 
form of fraction images. Many studies have shown that these 
fuzzy outputs are strongly related to the actual areal extents of 
classes on ground. For example, Fisher and Pathirana (1990), 
explored the use of MLC in fuzzy mode and showed a high 
correlation ranging from 47 % to 98 % between fuzzy outputs 
and actual class proportions on ground. Foody (1996) 
investigated the potential of ANN to derive land cover 
composition of mixed pixel and found significant correlations 
(> 80%) between ANN derived fuzzy outputs and class 
proportions on ground. Bastin (1997) used LMM, MLC and 
FCM to unmix the classes within a pixel and obtained 
correlation coefficients as 76% for LMM, 76.5% for MLC and 
83.4% for FCM. Focshi and Smith (1997) compared ANN and 
  
KB for classification of mixed pixels and showed that both the 
methods yielded significant improvements in detection of sub- 
pixel woody vegetations. In India, Kant and Sbadarinatii 
(1998), addressed the utility of LMM to generate fraction 
images of vegetation, soil, and water/shade in parts of Andhra 
Pradesh, Orissa, Madhya Pradesh and Maharashtra. 
These sample studies sufficiently demonstrate the potential of 
fuzzy methods for land cover classification from remote 
sensing data. However, obtaining fuzzy outputs through these 
methods in the allocation stage only partially solve the problem 
of mixed pixels. When the image is contaminated with a large 
number of mixed pixels, it may be hard to find desired number 
of pure pixels during training and testing stages of a 
classification. Therefore, mixed pixels need to be incorporated 
into all the stages of the classification. For example, in a study 
by Foody and Arora (1996), mixed pixels were accommodated 
in all three stages of a supervised classification performed by 
MLC, LMM and ANN. A significant improvement in 
correlation was observed when mixed pixels were used to train 
and test the classifier. The classification produced by 
accounting for mixed pixels in all its stages has been named as 
‘fully fuzzy classification’ (Zhang and Foody, 2001). 
This paper presents an overview of some fuzzy classification 
methods and also describes the ways to accommodate mixed 
pixels in all the stages of a classification. 
2. FUZZY CLASSIFICATION METHODS 
Generally, supervised image classification is applied that 
involves three stages; training, allocation and testing. The 
conventional crisp classification methods allocate each pixel 
into one class thereby producing erroneous results when applied 
on coarse spatial resolution images like IRS Wifs and NOAA 
AVHRR images that may contain mixed pixels. It is thus 
imperative that these images be classified at sub-pixel level to 
produce accurate land cover classifications. Now, some fuzzy 
methods to generate sub-pixel classifications are discussed. 
2.1 Fuzzy MLC 
! Corresponding author and Current address: Department of Electrical Engineering and Computer Sciences, Syracuse 
University, SYRACUSE, NY, 13210 (mkarora@syr.edu)