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Image Registration Based on CCRE for Remote Sensing Images
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Transcript of Image Registration Based on CCRE for Remote Sensing Images
Heaven’s Light is Our Guide
Rajshahi University of Engineering & Technology
Tentative Thesis Title: Image Registration
Based on CCRE for Remote Sensing
Images
Presented By-
Sarkar Sujoy Sarathi
Das
093022
Dept. of Computer
Science & Engineering
Supervised By-
Boshir Ahmed
Associate Professor
Dept. of Computer
Science & Engineering
Presentation Outline
Literature Review
Flowchart
Implementing CCRE
Forming Joint Histogram
Normalization
Calculating Similarity Measure
Implementing MI on same dataset
Performance measure with respect to
conventional MI
Conclusion & Discussions
Literature Review
Image Registration
Transforming different sets of data of two different
images in same co-ordinate system.
Inputs of this process are a pair of images
1. Target Image
2. Reference Image
In output, target image is aligned with reference
image in same co-ordinate system and known as
Registered Image.
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Image Registration Based on CCRE for
Remote Sensing Images
Literature Review(cont.)
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Target
ImageReference
Image
Registered Image
Figure 1: Image Registration Process[Aster Image of Tokyo bay area(Top Left) & Landset-7 Image of same area(Top
Right) courtesy of NASA]
Image Registration Based on CCRE for
Remote Sensing Images
Literature Review(cont.)
Multimodality of Image:
Two Images taken in same device/sensor-Uni-
modal
Two Images taken in different sensors- Multi-
modal
Image Registration Technique
Feature Based Registration
Intensity Based Registration
Feature based Registration:
Works by Feature detection & Feature matching in
both Images
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Image Registration Based on CCRE for
Remote Sensing Images
Literature Review(Cont.)
Intensity Based Image Registration:Works with intensity pattern of two images and complete the registration process.
Similarity Measure:Similarity measure quantifies how similar the intensity patterns of the two images are. Different Similarity Measures are-
1. Squared-Difference2. Squared Gradient Difference3. Mutual Information
We’ve used a better similarity measure known as Cross Cumulative Residual Entropy(CCRE) which is better than conventional Mutual Information method.
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Image Registration Based on CCRE for
Remote Sensing Images
Presentation Outline
Literature Review
Flowchart
Implementing CCRE
Forming Joint Histogram
Normalization
Calculating Similarity Measure
Implementing MI on same dataset
Performance measure with respect to
conventional MI
Conclusion & Discussions
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Image Registration Based on CCRE for
Remote Sensing Images
Flowchart
Iterative process
until Registration
completes
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Image Registration Based on CCRE for
Remote Sensing Images
no
yes
Presentation Outline
Literature Review
Flowchart
Implementing CCRE
Forming Joint Histogram
Normalization
Calculating Similarity Measure
Implementing MI on same dataset
Performance measure with respect to
conventional MI
Conclusion & Discussions
12/27/2014 9
Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE
Taking Input of Reference and Target
Image
Target ImageReference Image
The images above are 491 × 351 pixels in size with spatial resolution 30m. The images
from right side above are of band 4 (near infra-red) and band 5 (mid infra-red) (left side)
respectively from a set of Landsat Enhanced Thematic Mapper Plus (ETM+) data recorded
in the year 2000.) in centre of Canberra, Australia
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Figure 2: Inputs in Implementation Phase
Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Take a random region in reference Image
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Figure 3: Random region selection from reference image
Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Finding the same region in the distorted target image using the similarity measure algorithm CCRE. Experiment with same size different region in target image
Step-1:
Joint Histogram:
Based on intensity values of the pair of images joint histogram is formed
Figure 4: Joint histogram of target and reference
image12/27/2014 12
Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Step-2:
Normalizing:
Normalization of joint histogram is needed for
calculating P(u , v) stands for Joint Probability.
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Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Figure 5: Demonstration of step-2 (a) Joint
Histogram and (b) Joint histogram after
Normalization
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Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Step-3:
Joint Cumulative Residual Entropy:
Using the equation from P(u,v) we can find
𝑃 𝑡 ≥ 𝑢, 𝑟 = 𝑣 =
𝛿=𝑢
𝐿
𝑝 𝑡 = 𝛿, 𝑟 = 𝑣
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Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Calculation of CCRE:
𝑆𝐶𝐶𝑅𝐸 = 𝑢=1𝐿 𝑣=1
𝐿 𝑃 𝑡 ≥ 𝑢, 𝑟 =
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Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Figure 6: Minimum CCRE was found and calculated
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Image Registration Based on CCRE for
Remote Sensing Images
Implementing CCRE (Cont.)
Found Region in Target Image
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Region in Reference Image
Figure 7: Found Region in target image and co-
responding reference image
Image Registration Based on CCRE for
Remote Sensing Images
Presentation Outline
Literature Review
Flowchart
Implementing CCRE
Forming Joint Histogram
Normalization
Calculating Similarity Measure
Implementing MI on same dataset
Performance measure with respect to
conventional MI
Conclusion & Discussions
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Image Registration Based on CCRE for
Remote Sensing Images
Implementing MI on same
dataset Conventional Mutual information on same data set
was also performed using the equation below--
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𝑆𝑀𝐼 𝑇, 𝑅
=
𝑢 𝐿𝑇
𝑣 𝐿𝑅
[𝑃𝑡 = 𝑢,𝑟 = 𝑣
∗ log{𝑃 𝑡 = 𝑢, 𝑟 = 𝑣
𝑃𝑇 𝑡 = 𝑢 𝑃𝑅 𝑟 = 𝑣)}]
Image Registration Based on CCRE for
Remote Sensing Images
Implementing MI on same
dataset
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Figure 8: Minimum MI was found and calculated
Number of Iterations
MI
Valu
es
Image Registration Based on CCRE for
Remote Sensing Images
Presentation Outline
Literature Review
Flowchart
Implementing CCRE
Forming Joint Histogram
Normalization
Calculating Similarity Measure
Implementing MI on same dataset
Performance measure with respect to
conventional
MI
Conclusion & Discussions
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Image Registration Based on CCRE for
Remote Sensing Images
Performance Measurement
We’ve taken two dataset for performance analysis
shown below-
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a b
c d
Four multi-modal dataset of
datasets ETM+ image
captured around Canberra,
Australia (a) Band 4 (b)
Band 2 (c) Band 5 (d) Band
6 in 2001. First pair is (a)
and (b). Second pair is (c)
and (d)
Figure 8: Multi-model Datasets
Image Registration Based on CCRE for
Remote Sensing Images
Performance
Measurement(cont.)
We will measure maximum registration error usingthe equation below where
𝑥𝑖 and 𝑦𝑖 are the true locations where thecorresponding region should be found (actualregion) of the sample point on the target image and
𝑥′𝑖 and 𝑦′
𝑖 are the locations correspondingcalculated region which has been found by takingminimum value of CCRE values of the samplepoint on the target image which for a given pixel inthe scene
𝑀𝑅𝐸 = max 𝑥′𝑖 − 𝑥𝑖
2 − 𝑦′𝑖− 𝑦𝑖)
2)
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Image Registration Based on CCRE for
Remote Sensing Images
Performance
Measurement(cont.) After finding MRE we can calculate success rate
and plot in the graph below
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80
82
84
86
88
90
92
94
96
98
100
102
5 10 15 20
Su
ccess R
ate
Number of Regions
Success Rate for Dataset 1
CCRE
MI
88
90
92
94
96
98
100
102
5 10 15 Category 4
Su
ccess R
ate
Number of Regions
Success Rate for Dataset 2
CCRE
MI
Figure 9: Success rate for Dataset 1 & Dataset 2
Image Registration Based on CCRE for
Remote Sensing Images
Performance
Measurement(cont.) Failure to find the region in target image with
respect to exact region in target image is
considered as mismatched regions.
We will evaluate the number of mismatched region
using both method
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Image Registration Based on CCRE for
Remote Sensing Images
Performance
Measurement(cont.) Plotting Mismatched region number in terms of the
whole region region number we’ve examined
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0
1
2
3
4
5
6
7
5 10 15 20
Mis
matc
hed R
egio
ns
Number of Region Taken
Number of Mismatched region for Dataset 1
CCRE
MI
0
0.5
1
1.5
2
2.5
5 10 15 20
Mis
matc
hed R
egio
n
Number of region Taken
Number of Mismatched Region for Dataset 2
CCRE
MI
Figure 10: Mismatched regions count for Dataset 1 & Dataset 2
Image Registration Based on CCRE for
Remote Sensing Images
Presentation Outline
Literature Review
Flowchart
Implementing CCRE
Forming Joint Histogram
Normalization
Calculating Similarity Measure
Implementing MI on same dataset
Performance measure with respect to
conventional MI
Conclusion & Discussions
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Image Registration Based on CCRE for
Remote Sensing Images
Conclusion & Discussions
Limitations:
• Exhaustive Search
• Time Complexity
• Noise and Illumination changes rapidly
Future Works:
• Replacing the exhaustive search method in target
image by some form of heuristic search
• Work on Reducing iteration numbers that result a
lot of improvement with respect to time complexity
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Image Registration Based on CCRE for
Remote Sensing Images
Conclusion & Discussions
Conclusion:
◦ Significant criteria to consider when comparing registration
algorithms are Mismatched region and Success rate of
similarity measures
◦ The experimental results show that our proposed approach
of using cross-cumulative residual entropy (CCRE) as a
similarity measure provided a expressively higher success
rate than conventional MI
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Image Registration Based on CCRE for
Remote Sensing Images
References
[1]“Image registration methods: a survey” Barbara Zitova´, Jan Flusser, Image and Vision Computing.
[2] Ardeshir Goshtasby: 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications Wiley Press, 2005.
[3] Ian T. Young, Jan J. Gerbrands, Lucas J. van Vliet Delft University of Technology, Fundamentals of Image Processing Version-2.3.
[4] Gonzalez, R.C. and R.E. Woods, Digital Image Processing. 2009, Reading, Massachusetts: Addison-Wesley. 716.
[5] Silvio MONTRESOR, Université du Maine, Image Filtering: Fundamentals, 2007.
[6] The Basics: Images, Morten Larsen, Department of Basic Sciences and Environment Mathematics and computer science group, University of Copenhagen.
[7] X Jia, On error correction and accuracy assessment of satellite imagery registration. The Globe,2003.
[8] Mahmudul Hasan, Student Member, IEEE, Mark R. Pickering, Member, IEEE, and Xiuping Jia, Senior Member, IEEE Robust Automatic Registration of Multi-modal Satellite Images using CCREwith Partial Volume Interpolation.
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Image Registration Based on CCRE for
Remote Sensing Images
References
[9] J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis, 4th ed. Verlag: Springer, 2006, pp. 56-58.
[10] P. Viola and W. M. Wells III, “Alignment by maximization of mutual information,” in Proceedings of Fifth International Conference on Com-puter Vision, Cambridge, USA, 1995, pp. 16–23.
[11] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal, “Automated multimodality image registration based on information theory,” in Proceedings of 14th International Conference on Information Processing in Medical Imaging, France, 1995, pp. 263-274.
[12] C. Studholme, D. L. G. Hill, and D. J. Hawkes, “Automated 3-Dregistration of MR and CT images of the head,” Medical Image Analysis, vol. 1, no. 2, pp. 163–175, 1996.
[13] F. Wang and B. C. Vemuri, “Non-rigid multi-modal image registration using cross-cumulative residual entropy,” International Journal of Com-puter Vision, vol. 74, no. 2, pp. 201–215, 2007.
[14] M. Rao, Y. Chen, B. C. Vemuri and F. Wang, “Cumulative residual entropy: a new measure of information,” IEEE Transactions on Information Theory, vol. 50, no. 6, pp. 1220–1228, 2004.
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Image Registration Based on CCRE for
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Thanks everybody.
Q&A
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Image Registration Based on CCRE for
Remote Sensing Images