Remote Sensing Science February 2014, Volume 2, Issue 1, PP.1-7
Multi-source Image Registration Based on Logpolar coordinates and Extension Phase Correlation Jingguo Lv1#, Dehe Yang 2, Xiaona Wu 2 1.
Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation Beijing
University of Civil Engineering and Architecture, Beijing 100044, China 2. #
College Of Geoscience and Surveying Engineering China University of Mining & Technology, Beijing 100083, China
Abstract This paper introduces an algorithm of high precision about automatic image registration. It uses Moravec operators to extract image points and uses random sampling consistency algorithm to eliminate mismatching points; it is according to the feature points to get the rotation and zoom factor of basic image and warp image; it uses a algorithm which is of sub-pixel level and phase correlation to calculate offset after compensating ration and zoom cofficients; then uses interpolation algorithm to re-sample warp image. After the analysis of experiment, the designing algorithm processing has a high robustness and registration precision, and it is more suitable to the Multi-source of remote sensing image registration, compared to that of polynomial registration and Fourier-Mellin without deformation. Keyword: Feature Extraction; Logarithmic Polar Coordinate Transformation; Random Sampling Consistency; Image Registration; Phase Correlation
1 INTRODUCTION The data of multi-source remote sensing images which are obtained in different time, from different angles, views and sensors are the rich information source of the development of modern science and technology. It is applied to all kinds of fields, such as: earthquake disaster monitoring, land resources exploration and moving target recognition, etc. The image registration under a single sensor is looking for the offset of the same or similar area of two images which is large influenced by noise and gray level. Data that are obtained from many sensors are complementary to each other and redundancy, which contain more abundant information to reflect the comprehensiveness of features; the multisource remote sensing image registration is a method which uses the similarity measurement to compute transformation parameter of images and transform the images of different area into a same coordinate system, thus achieving the best pixel matching. There are different applications such as the feature orientation, detection change, and stereo vision and so on which need the sub-pixel level image registration, while the pixel level registration algorithm such as the cross-correlation registration and Fourier transform registration, are difficult to meet the requirements of the precise positioning. At present, the academia tends to study the sub-pixel level registration method which is divided into interpolation method, differential method and the largest mutual information method, etc. Interpolation method depends on the quality of the interpolation, and differential method depends on the sensitivity of light changing, while the largest mutual information method is unsuited to multi-source automation registration which requires human to select feature points. According to this problem, this paper describes a multi-source image automatic registration algorithm, and the following several steps is the process; (1) extracts feature points to get the point of the same name; uses random sampling consistency algorithm (RANSAC) to eliminate mismatching points; (2) transform logarithm polar coordinate to get rotation and scaling factors; (3) uses phase correlation to compute offset; uses interpolation to re1 http://www.ivypub.org/RSS
sample images staying registration.
1. ALGORITHM DESCRIPTION 1.1 Moravec feature extraction operator Moravec operator is the classic operator extracting from feature points, and calculates the squares of four diagonal adjacent pixels gray difference as the interest value in the image window (w, w) which sets each pixel as the center, given a experience threshold t and taking the average gray of whole image; select the points whose interest value is greater than the threshold value as candidate points; choose the point of largest interest value in a window of certain size as the feature point. It has the advantages of small calculation and will not lose gray information etc, and (w, w) is decided by the number of feature points. RANSAC is a mathematical model using iterative method to estimate the parameters, which is applied to image registration algorithm on the basic of characteristics and has the high ability of rejecting mismatching. First, according to the setting allowable error, it divides all the matching points into inner and outer points, using all the preliminary matching points; second, it uses rather accurate inner points to estimate parameters and eliminate the mismatching points. It has the advantages of reliability, high accuracy, robustness, etc.
Figure1. THE IMAGE BEFORE THE EARTHQUAKE(BASIC IMAGE)
Figure2. THE IMAGE AFTER THE EARTHQUAKE(IMAGE STAYING REGISTRATION)
1.2 Logarithm polar coordinate transformation algorithm The logarithmic polar coordinate transformation is transformed from Cartesian coordinate system into logarithmic polar coordinates system which translates rotation and scaling of image matching under Cartesian coordinate system -2http://www.ivypub.org/RSS
into translation transformation under logarithm polar coordinates system. Cartesian coordinate system: (
Logarithm polar coordinates system: ( )
( ) ( ) ( ) ) âˆš( ( ) Mapping translates the scaling and rotation changes in the axial into horizontal and vertical displacements under the logarithm polar coordinates, thus realizing the image mapping translates from a circular area to a rectangular area, namely two dimensions invariability of logarithmic polar coordinates. Scaling and rotation : Horizontal and vertical: (
Fugure3. THE ORIGINAL IMAGE Figure4. THE IAMGE AFTER POLAR COORDINATE TRANSFORMATION
Figure5. THE IMAGE TURNED 45 DEGREES(CLOCKWISE) Figure6. THE IMAGE AFTER POLAR COORDINATE TRANSFORMATION -3http://www.ivypub.org/RSS
Figure7. THE IMAGE ENLARGED 2 TIMES Figure8. THE IMAGE AFTER POLAR COORDINATE TRANSFORMATION
1.3 Phase correlation and the interpolation algorithm Phase correlation and interpolation algorithm improves image resolution by two images' interpolation. Then, performing the Fourier transformation uses leggy decomposition of frequency domain phase correlation to get the relationship among images, thus getting the registration results among images on sub-pixel level. The horizontal and vertical translation vector existing in two images is enlarged by certain times after interpolation to solve the problems that can't be solved by frequency non-integer domain phase correlation. Inverse Fourier transformation of two images before interpolation normalization mutual power spectrum gets unit impulse response function, which uses 2 d function sinc to approximate and gets coordinates of the peak that is phase correlation through function sinc, thus working out the vector of translation. Common interpolation algorithm includes: the most neighboring interpolation, bilinear interpolation method and three linear interpolation methods. Below is a function sinc.
Figure9.SINC DISPLAY ON 2 DIMENSIONS
Figure10. SINC DISPLAY ON 3 DIMENSIONS
1.4 Data sources The experiment data are all from remote sensing images, which are obtained from QuickBird full-color band in dujiangyan; and from high resolution digital images, which is obtained from ADS40 airborne three linear array push and type. Choose one period of the spectrum image to perform multi-source image registration experiment, thus getting a precision of 0.1 pixels on sub-pixel level registration. Then test the influence of gray level changing and anti-noise on the results of registration. It is turns out that registration based on Fourier transform has great robustness on gray level changing, and it also has high noise immunity. The registration method provided in this paper has high stability under rotation and scaling. -4http://www.ivypub.org/RSS
Extract feature points using Moravec
Eliminate mistake matching using RANSAC
Log-polar transformation based on feature points
Coefficients of rotation and zoom
Calculation of phase correlation to offset
Figure11. REGISTRATION FLOW CHART
1.5 Registration experiment
Figure12. THE IMAGE BEFORE THE EARTHQUAKE(QUICKBIRD) Figure13. THE IMAGE AFTER THE EARTHQUAKE(ADS40)
Figure14. THE IAMGE AFTER RESISTRATION -5http://www.ivypub.org/RSS
Dot mark 1 2 3 4 5 6
FEATURE POINTS ERROR(PART) Error X 0.28 -0.46 -0.38 -0.13 0.10 0.58
Error Y -0.65 1.14 0.98 0.28 -0.22 -1.54
RMS 0.71 1.23 1.05 0.31 0.24 1.64
RMS Error 0.98
1.6 Gray and noise influence on the registration Use the gray linear changing function D = a*T + b, in which ‘a’ and ‘b’ are factors of gray level changing, to translate the value of gray level from D to d. Besides, since noise has influence on registration, logarithm function of signal to noise ratio is used to add noise.
FIGURE15. THE IMAGE AFTER GRAY CHANGING TABLE II.
Figure16. THE IMAGE AFTER ADDING NOISE
THE INFLUENCE of GRAY AND NOISE on REGISTRATION ERROR
Gray changing Noise Note
SNR and RMSE 24 (db) 18 (db) 12 (db) 1.24 1.16 1.01 Gray and noise have small effect on registration
2 CONCLUSION Solving the problem of rotating, scaling and automation has become difficulty for multi-source image registration. This paper introduces the multi-source registration of logarithm polar coordinates and phase correlation sub-level pixel, and verifies the algorithm on image data. The experiment proves that: the algorithm in the paper takes the advantage of Moravec operator, RANSAC operator, logarithmic polar coordinate transformation and phase correlation algorithm, and realizes automatic registration of multi-source image on sub-pixel level, and solves the problem of gray changing, noise, rotating and scaling. In addition, because the high computation complexity of RANSAC and phase correlation has influence on image registration efficiency, so it is a necessary to take optimization on the running speed of the program.
ACKNOWLEDGMENT This paper is supported by Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation (No. 20111211N). Doctor Startup Fund (No.00331610015)
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Jingguo Lv(1973-) from Pingdu county in Shandong Province, Main research direction: Mining of Remote Sensing Information,
Processing of Remote Sensing Image. Email: email@example.com 2
Dehe Yang(1985-) from Taian county in Shandong Province, Main research direction: I mage Segmentation. Email: firstname.lastname@example.org