ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
THE APPLICATION OF NEURAL NETWORK AND FUZZY SET TO CLASSIFICATION OF 
REMOTELY SENSED IMAGERY 
1 2 1 
Dongmin HUO , Jingxiong ZHANG , Jiabing SUN 
(1 School of Remote Sensing and Informatics Engineering, Wuhan University, Wuhan 430079,China) 
(2 National Key Lab of Information Engineering for Surveying and Mapping and Remote Sensing, 
Wuhan University, Wuhan 430079, P. R. China 
KEY WORDS: fully fuzzy; remotely sensed imagery; supervised classification; neural networks; land cover 
ABSTRACT 
This paper presents a fully fuzzy strategy for image classification based on a neural network architecture, which promise to overcome 
some problems encountered in a conventional crisp even fuzzy classification process. The proposed method was successfully applied in 
a classification of land cover with results confirming the flexibility and practicality of this fully fuzzy approach. 
INTRODUCTION 
The technology of RS (Remote Sensing) is widely applied 
to the resource management and environment monitoring. And 
the image classification is a very important application aspect. 
Generally speaking, the classification of remotely sensed image 
is performed by means of visual interpretation or computer 
automation or semi-automation. Firstly, the procedure of 
classification needs an appropriate classification category 
system according to the requirement and ground fact. Next, 
every pixel is classified into a certain category by choosing a 
classification method. Finally, the average classification 
accuracy is calculated by forming an error matrix according to 
the other group of independent reference data. The prerequisite 
of this conventional crisp classification is that every pixel of the 
image and the reference data used for estimating the 
classification accuracy are supposed to have a unique category 
affiliation. That is to say, suppose the real world can be clearly 
defined and measured. In fact, the real world itself is a huge and 
complicated multi-dimension system. Among it, so many natural 
or semi-natural phenomena can’t easily be classified into some 
certain categories. Although the spatial resolution of Remotely 
Sensed image is being improved, there are still many pixels 
whose spectrum characteristic is the combination result of 
response from their neighbors. Besides, the phenomena which 
are ‘the same spectrum from different features' or 'the same 
features have different spectrum’ are still widely existing. They 
lead to the serious limitation of the traditional classification 
method. Therefore, this problem of classification method cannot 
be resolved only on the basis of statistical analysis. 
The theory of fuzzy set is a big hit in the field of RS 
classification at present. According to the theory of fuzzy set, an 
element or an object, such as a pixel of the RS image, has the 
fuzzy affiliation relationship. Many experiments prove that the 
fuzzy method can express the ambiguous phenomena and their 
characteristics more efficiently than the conventional 
classification method does. But the fuzzy classification method 
ignores the existing ambiguity of the training process in the 
supervised classification. It implies that the training sample is 
supposed to be made up of a group of representative objects or 
pixels, which can be clearly defined and classified. This actually 
follows the conventional method. So it is not the genuine fuzzy 
classification. 
This paper analyses a so-called method of fully fuzzy 
supervised classification by means of the brief mathematical 
description about the classification procedure. Utilizing the 
neural network which has a good mechanism of learning and 
generalization and the capability of anti-variance and easily 
expanding to a dynamic system, this paper designs a fully fuzzy 
classification system. And the system is applied to the land cover 
classification of the imagery (Landsat TM) in the study area of 
the Delta of the Yellow River in China. This paper also provides 
the accuracy evaluation of land cover classification. Its efficiency 
can be proved. 
THE PROCEDURE OF IMAGE CLASSIFICATION AND FULLY 
FUZZY STRATEGY 
Generally speaking, every pixel in the unclassified image is 
an object, which belongs to an element or a class of the 
category set {1,2,•••,<?} . It has some observation values, 
such as spectral brightness and texture character, forming an 
eigenvector X . Supposed that the probability of this pixel 
belonging to class /(/ G (1,2,• • *,c}) is 7l l and the 
probability distributing density of the eigenvector X belonging