Open Access
Subscription Access
Co-Registration of LISS-4 Multispectral Band Data Using Mutual Information-Based Stochastic Gradient Descent Optimization
We propose a solution for automatic co-registration of LISS-4 MX radiometrically conditioned multi-spectral images issue by considering an optimization problem in which mutual information-based approach is used. Co-registration of multi-spectral images from the same sensor may also be a tough problem to tackle, whenthe payload imaging geometry is complex. The multi-spectral images acquired by ISRO Resources at-1/2 LISS-4 MX class of sensors pose such problems and demand an automatic registration solution for system corrected product generation to cater to user needs. Optical remote sensing image registration is assisted by image geo-referencing or navigation information along with components such as feature detection, matching, correspondence, and resampling the input image to the reference geometry. Intensity-based methods employ an iterative registration framework,where similarity metric based image matching and correspondence is refined to find out optimum transform parameters. We could successfully employ mutual information-based adaptive stochastic gradient descent optimization algorithm to do sub-pixel level satellite image registration tasks by a careful choice of parameters and models related to metric, transform, optimizer, and interpolator in a robust image registration framework which is automatic for different terrain data. The performance is also compared to a recent scale invariant feature transform (SIFT)-based registration method.
Keywords
Image Registration, LISS-4, Mutual Information, Optimization.
User
Font Size
Information
- ISRO, Resourcesat-2 Data User’s Handbook, NRSA Report No. NRSC: SDAPSA: NDCNDC: DEC11-364, 2011, Hyderabad, India.
- Manthira, M. S., Kayal, R., Ramakrishnan, R. and Srivastava, P. K., RESOURCESAT-1 LISS-4 MX bands on ground co-registration by in-flight calibration and attitude refinement. Int. J. Appl. Earth Obs. Geoinf., 2008, 10, 140–146.
- Radhadevi, P. V., Solanki, S. S., Jyothi, M. V., Nagasubramanian, V. and Geeta, V., Automated co-registration of images from multiple bands of Liss-4 camera. ISPRS J. Photogramm. Remote Sensing, 2009, 64, 17–26.
- Pillala, S. K., Ravikanti, C., Mishra, N., Janja, S. and Geeta, V., A generalized search scheme for automatic registration of remote-sensing data. Int. J. Remote Sensing, 2012, 33, 490–501.
- Brown, L. G., A survey of image registration techniques. ACM Comput. Surv., 1992, 24, 325–376.
- Fonseca, L. M. G. and Manjunath, B. S., Registration techniques for multisensor remotely sensed imagery. Photogramm. Eng. Remote Sensing, 1996, 62, 1049–1056.
- Zitova, B. and Flusser, J., Image registration methods: a survey. Image Vis. Comput., 2003, 21, 977–1000.
- Maintz, J. B. A. and Viergever, M. A., A survey of medical image registration. Med. Image Anal., 1998, 2, 1–36.
- Pluim, J. P. W., Maintz, J. B. A. and Viergever, M. A., Mutual-Information-based registration of medical images: a survey. IEEE Trans. Med. Imaging, 2003, 22, 986–1004.
- Maes, F., Collignon, A., Vandermeulen, D., Marchal, G. and Suetens, P., Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging, 1997, 16, 187–198.
- Viola, P. and Wells III, W. M., Alignment by maximization of mutual information. Int. J. Comput. Vis., 1997, 24, 137–154.
- Thevenaz, P. and Unser, M., A pyramid approach to sub-pixel image fusion based on mutual information. In Proc. IEEE Int. Conf. Image Processing, Lausanne, Switzerland, 1996, vol. 265–268, pp. 16–19.
- Cole-Rhodes, A. A., Johnson, K. L., LeMoigne, J. and Zavorin, I., Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient, IEEE Trans. Image Process., 2003, 12, 1495–1511.
- Klein, S., Pluim, J. P. W., Staring, M. and Viergever, M. A., Adaptive stochastic gradient descent optimisation for image registration. Int. J. Comput. Vis., 2009, 81, 227–239.
- Klein, S., Staring, M., Murphy, K., Viergever, M. A. and Pluim, J. P. W., Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imag., 2010, 29, 196–205.
- Mattes, D., Haynor, D. R., Vesselle, H., Lewellen, T. K. and Eubank, W., PET–CT image registration in the chest using freeform deformations. IEEE Trans. Med. Imag., 2003, 22, 120–128.
- Unser, M., Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag., 1999, 16, 22–38.
- Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O. and Hawkes, D. J., Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag., 1999, 18, 712–721.
- Goshtasby, A. A., Registration of image with geometric distortion. IEEE Trans. Geosci. Remote Sensing, 1988, 26, 60–64.
- Plakhov, A. and Cruz, P., A stochastic approximation algorithm with step size adaptation. J. Math. Sci., 2004, 120, 964–973.
- Li, Q., Wang, G., Liu, J. and Chen, S., Robust scale-invariant feature matching for remote sensing image registration. IEEE Geosci. Remote Sensing Lett., 2009, 6, 287–291.
Abstract Views: 363
PDF Views: 137