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]



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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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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Literature Review

Flowchart

Implementing CCRE


Normalization




conventional MI


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Flowchart

Iterative process

until Registration

completes

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no

yes


Literature Review

Flowchart

Implementing CCRE


Normalization




conventional MI


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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



Implementing CCRE (Cont.)

Take a random region in reference Image

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Figure 3: Random region selection from reference image




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




Step-2:

Normalizing:

Normalization of joint histogram is needed for

calculating P(u , v) stands for Joint Probability.

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Figure 5: Demonstration of step-2 (a) Joint

Histogram and (b) Joint histogram after

Normalization

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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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Calculation of CCRE:

𝑆𝐶𝐶𝑅𝐸 = 𝑢=1𝐿 𝑣=1

𝐿 𝑃 𝑡 ≥ 𝑢, 𝑟 =

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Figure 6: Minimum CCRE was found and calculated

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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




Literature Review

Flowchart

Implementing CCRE


Normalization




conventional MI


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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)}]



Implementing MI on same

dataset

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Figure 8: Minimum MI was found and calculated

Number of Iterations

MI

Valu

es




Literature Review

Flowchart

Implementing CCRE


Normalization




conventional

MI


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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



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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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



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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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




Literature Review

Flowchart

Implementing CCRE


Normalization




conventional MI


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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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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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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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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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Thanks everybody.

Q&A

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