Full text: Mapping without the sun

84 
TEXTURE CLASSIFICATION RESEARCH 
BASED ON LIFTING-BASED DWT 9/7 WAVELET 
Hong Zhang a ’ *, Ning Shu a 
a School of Remote Sensing Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 
Email: hongzhangwh@163.com 
Modified update Ui 
jlSdf+a], 
a ‘{ S(d}+dl 
Modified update P1 
, l a ( X 2i + X 2i 
'{2ax 2i +x 2i 
KEY WORDS: lifting-based DWT 9/7 wavelet; Wavelet statistical features (WSFs); Wavelet co-occurrence features (WCFs); 
Feature extraction; Texture classification 
ABSTRACT: 
Texture classification analysis plays an important role in remote sensing image. The main difficulty of texture analysis in the past 
was the lack of adequate tools to characterize different scales of textures effectively. The 2-D lifting-based DWT 9/7wavelet filter is 
used here, without additional computations, giving lifting-based architectures a significant advantage over convolutional filter band- 
based architectures. This paper describes the texture classification using (1) the known texture images are decomposed using 
9/7wavelet. Then, mean and standard deviation of approximation and detail sub-bands of 3- level decomposed images are calculated. 
They are wavelet statistical features (WSFs) (2) In order to improve the correct classification rate further, it is proposed to find co 
occurrence matrix features for original image, approximation and detail sub-bands of 1-level 9/7 wavelet decomposed images. The 
various co-occurrence features such as contrast, energy, entropy and homogeneity are calculated from the co-occurrence matrix. 
These are wavelet co-occurrence features (WCFs). (3) At last, the combination of WSFs and WCFs (feature vector) are used to 
classify images. From the exhaustive experiments conducted with texture images, it is found that (1) using lifting-based DWT 9/7 
wavelets filter, it is easily adapted to arbitrary remote sensing image sizes, yielding texture classification analysis system that is 
small in size, low in memory, and high in performance. (2) At the same time, the mean success rate is improved for a combination of 
WSFs and WCFs compared to that of WSFs (or) WCFs. 
1. INTRODUCTION 
As we all known, texture classification analysis plays an 
important role in remote sensing image. The main difficulty of 
texture analysis in the past was the lack of adequate tools to 
characterize different scales of textures effectively. The 2-D 
lifting-based DWT 9/7wavelet filter and Gray Level Co 
occurrence Matrix is used here, we can get the curve of from 
the average energy of sub-image of used land samples, and 
texture classification parameters of some kind of land 
samples.Part two give the introduction of CDF 9/7 wavelet, 
part three give the selecting texture structure image character 
method based on wavelet transform and Gray Level Co 
occurrence Matrix ,and part four give the result. 
2. INTRODUCTION OF CDF 9/7 WAVELET 
Lifting scheme can design the wavelet transforms that map 
integer to integer, which provides the effective tool for studying 
the lossless image compression. The method for wavelet 
coefficients coding is a key technique to implement image 
compression, which not only affects the effect of compression, 
but also the qualities of resuming image and the time of coding 
and decoding. It not only inherits the characteristic of the first 
generation wavelet, but is independent of Fourier transform and 
particularly easy to build on non linear wavelet transforms, such 
as integer wavelet transforms. 
The Cohen-Daubechies-Feauveau 9/7 (CDF 9/7) wavelet 
transform is an especially effective biorthogonal wavelet, used 
by the FBI for fingerprint compression and selected for the 
JPEG2000 standard. CDF 9/7 wavelet have good property, 
using lifting scheme method, map integer to integer is come 
true. 
The biorthogonal 9/7 wavelet can be implemented as four 
lifting steps followed by scaling; a lifting scheme requires that 
the following equations be implemented in hardware: 
predict PI: d) = a(x 2i + x 2i+2 ) +x 2i+x 
update U1: a ) = (3{d) + d)_ x ) + x 2i 
predict P2: df = y(a\ + a) +x ) + d) 
update U2: a f = S(df + d*_ x ) + a] 
Scaled: a i =gaf 
Scale G2: d, = d] / g 
The original data to be filters is denoted by Xi, and the 2-D 
DWT outputs are the approximation coefficients and details 
coefficients. 
When the biorthogonal 9/7 wavelet is implemented with 
convoltional filter, the image is extended by four pixel on all 
sides, the simple way of implementing symmetric extension is 
used, and the update step equations are modified to change the 
calculation for the first pixel in each row and column so that 
Modified update U1: 
fi(d\ + d)_ x ) + x 2l , i = 1,2,..., N / 2-1 
* Corresponding author. 
Modified update P2 
2 _ Y( a ) +a '>l 
'{2ya]+d 
There are some 
0.05298011854, 1 
4 =1.149604398. 
L*L image 3 re 
follows: 
image f(x,y 
(a) original im 
112 
HL2 
LH2 
HH2 
LH1 
\ 
(c) second level dec 
Figure 1. 2-d DWT 
Thereinto: LL: app 
HL: plai 
LH: ven 
HH: dia 
3. EXTRACT] 
FEATURE BAS1 
GRAY let 
Generally, the resu 
character measure 
wavelet transform 
texture is character 
a knighthood, the 1 
is repeated over the 
°r texel. Selecting 
wavelet transform 
useful.
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.