Xiaorong Xue

1. The Research on Parallel Fusion of Remote Sensing Images and Its Applications Xiaorong Xue School of Computer and Information Engineering, Anyang Normal University Henan,China

2. Outline • Importance of Remote Sensing Image Fusion • ParallelRemote Sensing Image Fusion • A Serial Fusion Method of Beijing-1 Micro-Satellite Remote Sensing Images Based on Principal Component Analysis(PCA) • The Process about the Parallel Algorithm of Beijing-1 Micro-Satellite Remote Sensing Image Fusion • Experiments and Conclusion

3. Importance of Remote Sensing Image Fusion • With the development of modern remote sensing technology • The types of Remote sensing data are increasing a variety of earth observation satellites different spatial resolution, time resolution, spectral resolution • the remote sensing data obtained is increasing with great capacity.

4. Importance of Remote Sensing Image Fusion • How to play the advantages of a variety of remote sensing data In the description of the same object with multiple-source remote sensing information, how to conduct effective data processing, the application of remote sensing image fusion is a very important choice.

5. Importance of Remote Sensing Image Fusion • Remote sensing image fusion According to a certain algorithm, the images of the same scene obtained from different sensors, or the images of the same scene obtained from the same sensor at different times are synthesized into one image which can meet certain requirement. • The meanings of image fusion • It can improve system reliability and robustness. • It can extend the observation range of space and time. • It can improve the accuracy and credibility of information. • It can improve performance of target monitoring and identification. • It can reduce the redundancy investment of the system

6. Types of remote sensing image fusion • Pixel fusion data layer integration of the raw data collected directly • Feature level fusion The first step is feature extraction of the original remote sensing image information, and then comprehensive analysis and processing of the features are done so as that the fusion result can give the maximum feature information required for decision making. • Decision level fusion a high level of integration, which is the image information identification, classification or target detection on the basis of information provided by the pixel-level and feature-level fusion, the integration results obtained can directly provide the basis for the command, control and decision-making system. The research showed in the presentation is about the pixel level fusion of Beijing-1 Micro-satellite remote sensing images.

7. ParallelRemote Sensing Image Fusion • The reasons for the development of rapid remote sensing image fusion. • When remote sensing data is large, fusion is large in computing capacity and time-consuming, so it is difficult to carry out rapid, real-time fusion. • However, in some remote sensing applications, such as disaster monitoring, prevention and relief, dynamic monitoring on plant diseases and insect pests of agriculture, etc., rapid even real-time fusion is required. • How to realize accurate and fast remote sensing fusion is more and more urgent.

8. Parallel Remote Sensing Image Fusion • Our research Based on the advantages of parallel computing in the high-performance computing, effective parallel methods of remote sensing image fusion, and adaptive data division methods are studied. And measures of improving the parallel integration efficiency are also discussed. At the same time, research about the application of the rapid remote sensing data fusion methods in practice(such as disaster monitoring, prediction and relief, land use, geological survey, ecological environment monitoring, agriculture, forestry, etc.) are studied with same emphasis.

9. Beijing-1 Micro-satellite • In one of our research projects, we do research and application of processing of images from Beijing-1 Micro-satellite • Beijing-1 Micro-satellite is one of the Chinese small satellites that run well and play important roles, it was successfully launched in China in 2005, it is a high-performance small satellite for earthobservation, the resolution of its panchromatic band sensor of 4 meter is currently the highest among Chinese small satellites, and it can provide a large amount of remote sensing data with high resolution.

10. Star sensor SBAND Telemetering antenna Solar panel Solar panel XBAND data transmission antenna The SBAND instruction Injection antenna The SBAND instruction Injection antenna SBAND data transmission antenna SBAND telemetry antenna Panchromatic remote sensing sensor Multi spectral remote sensing sensor Fig.1 the satellite appearance of Beijing-1 Micro-satellite

11. it can provide a large amount of remote sensing data with high resolution. • To play its role better, the importance and necessity of selecting the image data fusion technology. • in some applications of Beijing-1 Micro-satellite such as disaster prevention and disaster relief, etc., it is necessary to extract useful information quickly and timely through remote sensing image fusion. • It is very important to explore the rapid image fusion technology for playing the role of Beijing-1 Micro-satellite. • In the high-performance computing, parallel cluster computing system has a higher cost-effective ratio and good expansibility, which can meet the different large-scale computing problems.

12. Fig.2 A panchromatic image Fig.3 The corresponding multi-spectral image

13. Fig.4 The fusion image

14. Fig.5　 Beijing-1 Micro-satellite image in peripheral areas of Wenchuan earthquake

15. Fig.6 part of Beijing-1 Micro-satellite image in the surrounding areas of the Wenchuan earthquake

16. A Serial Fusion Method of Beijing-1 Micro-Satellite Remote Sensing Image Based on Principal Component Analysis(PCA) • The basic steps of the remote sensing image fusion based on principal component analysis (PCA) • The multi-spectral image after registration is transformed with PCA, and the first principal component PC1 is extracted. • The panchromatic band image are stretched. • PC1 is replaced with the stretched panchromatic band image, the inverse PCA transformation is done, and the fused image is gotten.

17. Fig. 7 The flow chart about the serial method of Beijing-1 Micro-Satellite remote sensing image fusion Based on principal component analysis (PCA)

18. The two Beijing-1 Micro-satellite images after registration, the panchromatic and multi-spectral images, are adjusted to the same image size, the three band (R, G, B) data of the multi-spectral image are input as a matrix, the size of multi-spectral image is that its height is H, its width is W, the size is H * W, and then the size of the input matrix IM is 3 * (H * W).

19. The covariance matrix algorithm (x=1,2,3；y=1,2,3) Xi, Yi respectivelyrepresent one of the R, G, B band value of the i pixel.

20. How to get effective parallel algorithm from the serial algorithm?

21. Which links are intensive in computation? • Is parallelization can be done for those links, or some of those links? • How to do parallelization ?

22. The Process about the Parallel Algorithm of Beijing-1 Micro-Satellite Remote Sensing Image Fusion • The thinking of parallel PCA fusion is as the following • According to the number of processes, image data is distributed, the global means of R, G, B component of multi-spectral image are obtained with parallel computing. • According to the formula of the parallel covariance matrix, covariance matrix of multi-spectral image is gotten by parallel computing.

23. Eigenvalues and eigenvectors of the covariance matrix are calculated, and eigenvectors are sorted according to the magnitude of absolute values of eigenvalues. • The PCA transformation of multi-spectral image data is done with parallel computing, and the first principal component after transformation is replaced by the stretched panchromatic band image. • Inverse PCA transformation is done with parallel computing. • Form the fused image.

25. Experiments and Conclusion • Experiment data: the three-band multi-spectral image and panchromatic image of Beijing-1 Micro-satellite in the same area • Test platform:8 computers constitute a cluster architecture (one as the management node (also do computing ), 7 computing nodes), each node is configured as the following, its CPU is the Intel P Core(D) G2030, memory is 2G, the 8 computers are interconnected through the 100Mbps Fast Ethernet Switch, and the software environment is made up of the WindowsXP operating system, parallel libraries of message passing, NT-MPICH, • the algorithm's parallel performance

26. Fig. 9 the multispectral image of Beijing-1 Micro-satellite Fig. 10 the fusion result image of remote sensing images from Beijing-1 small satellite with the proposed method of image fusion Fig.8 the panchromatic image of Beijing-1 Micro-satellite

27. Fig. 11　 The panchromatic image of Beijing-1 Micro-satellite

28. Fig. 12　the multi-spectral image of Beijing-1 Micro-satellite

29. Fig.13 the fusion result image of remote sensing images from Beijing-1 small satellite with the proposed method of image fusion

30. Table 1. the parallel efficiency (Efficiency＝ running time on n computers/ running time on one computer)

31. Conclusion • To play better role of remote sensing in some fields such as disaster monitoring , a fast fusion method of remote sensing images from Beijing-1 Micro-satellite is studied and realized in the environment of distributed memory. • The parallel fusion method is tested with different sizes of multi-spectral and panchromatic images of Beijing-1 Micro-satellite. The results show that the new algorithm has better convergence results and good parallel performance, and the wider the network bandwidth, the higher the efficiency, and the better the extensible function.

32. Thank you very much