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Title
Mapping without the sun
Author
Zhang, Jixian

THE APPLICATION RESEARCH IN ASSISTANT CLASSIFICATION OF REMOTE
SENSING IMAGE BY TEXTURE FEATURES COMBINED WITH SPECTRA FEATURES
Y.M. Fang, X.Q. Zuo, Y.J. Yang, J.H. Feng
Faculty of Resource, Kunming University of Science and Technology, No.253 Xuefu Road,
Kunming Yunnan, China-fangyuanmin@126.com
Commission VI, WG VI/4
KEY WORDS: Image, Assistant Classification, Spectrum Feature, Texture Feature, Gray Level Co-occurrence Matrix
ABSTRACT:
With the development and application of high resolution remote sensing satellite, more clear textures occur in the remote sensing
image. The features of land forms and features reflected by textures are important information in distinguishing ground objects.
Based on original image, adding texture features can promote veracity and accuracy of classification. If, especially, the spectra
feature of different objects is nearly similar, texture features will play an important role in distinguishing these objects. For the
assistant classification of remote sensing image, the methods, that the texture feature images are extracted by the gray level co
occurrence matrix and the classifications are carried out by combined the texture features with the spectral features, is researched in
this paper. Extracting the texture feature images are realized by the computer program developed by us. The test results show that the
assistant classification as the paper mentioned could increase the classification veracity and accuracy of remote sensing images. Also
the results are analyzed and compared with the traditional ways.
1. INTRODUCTION
Automatic recognition of remote sensing image is a great chal
lenge in the field of remote sensing, computer vision and vague
recognition. With the rapid development of remote sensing
technology, the automatic extraction of image information from
remote sensing images has become a means of interpretation of
remote sensing images. However, lower accuracy of current
computer classification is difficult to deal with the map of lar-
ger-scale and medium-scale. Therefore, enhancing the preci
sion of special classification is the core of research on remote
sensing technology and application.
If the remote sensing image classification applies spectrum-
feature-based classification method only, the wrong classifica
tion on the phenomenon of the same thing but different spectra
and the same spectrum but different things will be created in
evitably. With the development and application of high resolu
tion remote sensing satellite, texture features can be represented
more clearly in the remote sensing image. In theory, the texture
feature as a band participating in spectrum feature classification
may effectively avoid the wrong classification caused by the
phenomenon of the same spectrum but different things and the
same thing but different spectra, which can promote the reli
ability and the accuracy of classification. The research in this
paper confirmed that adding the texture feature information to
carry on the special classification is an effective way to in
creases the precision on basis of the remote sensing spectrum
information. This classification method can extract special im
age which is significant to the classification application of me
dium scale or small scale image figure. 2
we applied has already been pre-processed which is allowed to
do the following experiment directly. The image has contained
many land sorts such as the inhabited area, forest, bare land,
paddies and lakes, which can check the classification effect sat
isfactorily.
Figure 1. SP0T5 remote sensing images of this article
adopted
3. EXTRACTION OF THE TEXTURE FEATURES
IMAGES
3.1 Gray Level Co-occurrence Matrix
The gray level co-occurrence Matrix, begins with the pixel of
image (x, y) gray as i, is the probability P (i,j,S,9) that will
appears simultaneously with pixel(x+Ax, y+Ay) with the
distance as 8 and the gray as j. It is showed in following figure.
2. THE BASIC SITUATION OF IMAGE
Figure 1 is the remote sensing image used in this article which
is the spot5 image. This image is fused with 2.5 meters resolu
tion panchromatic data and 10 meters resolution multi-spectra
image. The size of image is 760x490 pixels. This part of image