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Title
Mapping without the sun
Author
Zhang, Jixian

284
LAND COVER CLASSIFICATION BY IMPROVED FUZZY C-MEAN CLASSIFIER
ZHAO Quan-hua, SONG Wei-dong, Bao Yong
School of Geomatics, Liaoning Technical University, Fuxin 123000,China
zqhlby@163.com
Commission VI, WG VI/4
KEY WORDS: land cover; improved fuzzy c-mean classifier; principal component analysis; TM data; confusion matrix;
ABSTRACT:
Result from uncertainty and mix pixel of most remote sensing information, spectral information of some pixels are fuzzy in the
classification process of land cover. In most time, we can’t ascertain some pixels to be one or the other, because of it, it is hard to get
satisfied classification results by traditional hard classification method. Then we can adopt soft classification method such as fuzzy
clustering in the classification process of land cover. With Satellite Liaoyang panchromatic image, improved fuzzy c-mean Classifier
was used to land cover classification in this paper. First samples of three kinds of features were selected from the panchromatic image
considering the spectral information of features. Then each average of contrast, entropy and variance of histogram characteristics
were computed as clustering center of fuzzy clustering. 2.5 was selected as weight index,3 as clustering number, Mahalanobis
distance scale which can detect characteristic space for data muster of super ellipse structure was applied in the fuzzy clustering. The
three kinds of features were ascertained through priori knowledge after fuzzy clustering. At last, confusion matrix was introduced in
precision evaluation. The result shows that the classification method based on fuzzy c-mean classifier is a high precision method for
land cover classification.
precision method for land cover classification.
1. INTRODUCTION
Land cover classification of remote sensing image is a process
of detecting and identifying types of object based on the
characteristics of spectrum, texture and geometric. The
classification is a important step of image recognizing and
interpreting because it is necessary for information extraction
from remote sensing image, detecting of dynamic
transformation, making thematic map and establishment of
remote sensing database. So land cover classification has a
bright future. Satellite images data is always enormous
because of the characteristic of multi-band, multi-spatial
resolution, multi-time resolution. Some pixels of image data
are always fuzzy and uncertain because of the complexity of
the feature spectrum and the diversity of disturbing factors. So
in the process of land cover classification, it difficult to
ascertain some pixels to be one or the other. In order to
improve the accuracy of classification, soft classification is
necessary to ascertain the membership for each pixel.
Traditional classification method is not perfect because it is
based on the statistical relations between statistical
characteristics and training sample datas. Fuzzy classification
is based on the hypothesis that each pixel involved many
features, but membership of each feature is different.
Membership of training samples is ascertained in the process
of training classifier. So fuzzy clustering method can get high
precision for land cover classification. In the paper, improved
fuzzy c-mean classifier was used to land cover classification
based on Matlab. At last, confusion matrix was introduced in
precision evaluation. The result shows that the classification
method based on improved fuzzy c-mean classifier is a high
2. IMPROVED FUZZY C-MEAN CLUSTERING
Fuzzy c-mean clustering was proposed on the basis of k-mean
method and fuzzy theory. Euclidean distance is adopted in
Routine fuzzy c-mean classifier. It’s a method of all-direction
uniform. But the pixels of remote image do not distribute like
that showed by scatter diagram of pixels. So it always can not
get ideal result expected. In order to bring the advantages into
full play of fuzzy c-mean classifier, a new defined distance
should be introduced in it. So improved fuzzy c-mean
classifier was introduced in this paper. Its basic theory is
described as
follows:
n is the number of pixels for clustering,
X = fo,* 2 ,
represent the muster of all pixels, in
X = {x,,* 2 .
the muster,
vector , c is the classification number.
is the clustering center for each class,
is the muster of clustering center of all classes.
, p is the number of feature
v , =(v!,vf,...v' )
V = {v l ,v 2 ,...,v c ]
represent the membership of pixel
belong to
number 1 . The membership array is defined as follows:
U = [u ik ]
L IK Jcxn /1 \