A New Comparative Study of Radiometric Correction on Satellite Images Using Kalman Filter and Levenberg Marquardt Algorithm
Keywords:Satellite Image, Radiometric Correction, Kalman Filter, Levenberg Algorithm, prediction
With the development of satellite and remote sensing techniques, more and more multi-temporal image data from airborne/satellite sensors have been collected and used in huge amounts to monitor the changes in land use and land cover. Radiometric consistency among collected multi-temporal imagery is difficult to maintain, because of variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle. Radiometric corrections are used to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. In this paper, a comparative analysis of radiometric correction of satellite images is made between Kalman filter and Levenberg algorithm. In first phase, the satellite images such as Landsat, Liss-3 have been corrected using Kalman filter technique. In the second phase, by using Levenberg algorithm radiometric correction has been performed. After that comparative study is made between the results of both techniques using different performance measures such as completeness, correctness and quality.
Priti Tyagi, Udhav Bhosle,”Ätmospheric correction of Remotely sensed Images in Spatial and Transform Domain”, International Journal of Image Processing, Vol(5), Issue (5), pp 564-579, 2011
Seema Biday, and Udhav Bhosle, “Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery”, International Journal of Civil and Environmental Engineering, Vol.2, No.3, 2010.
Mirnalinee Dhinesh, Sukhendu Das and Koshy Varghese, "Automatic Curvilinear Structure detection from Satellite Images using Multiresolution GMM ", International Journal of Imaging Science and Engineering (IJISE),Vol.2, No.1, pp. 154-157, 2008.
Michael Schroder, Hubert Rehrauer, Klaus Seidel, and Mihai Datcu, “Spatial Information Retrieval from Remote-Sensing Images—Part II: Gibbs–Markov Random Fields”, IEEE Transactions on geoscience and remote sensing, Vol. 36, No. 5, pp.1446-1455, 1998.
T Rajani Mangala and S G Bhirud, “An Effective ANN-Based Classification System for Rural Road Extraction in Satellite Imagery”, European Journal of Scientific Research, Vol.47, No.4, pp.574-585, 2010.
Rajiv Kumar Nath and S K Deb, “Water-Body Area Extraction from High Resolution Satellite Images-An Introduction, Review, and Comparison”, International Journal of Image Processing (IJIP), Vol. 3, No.6, pp.353-372, 2010.
Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman, and Henda Ben Ghezala, “Improving Spatiotemporal Change Detection: A High Level Fusion Approach for Discovering Uncertain Knowledge from Satellite Image Databases”, World Academy of Science, Engineering and Technology, Vol. 52, No.12, pp 52-57, 2009.
Sarah C. Goslee, “Analyzing Remote Sensing Data in R: The landsat Package”, Journal of Statistical Software, Vol. 43, No.4, pp.1-25, 2011.
Salem Saleh Al-amri, N.V. Kalyankar and Khamitkar S.D, “A Comparative Study of Removal Noise from Remote Sensing Image”, IJCSI International Journal of Computer Science Issues, Vol. 7, No. 1, pp. 32-36, 2010.
Ellison, J. and Milstein, J, “Improved Reduced-Resolution Satellite Imagery”, In proceedings of Goddard Conf. on Space Applications of Artificial Intelligence & Emerging Info. Technologies in Los Angeles, CA, 9-11 May 1995, pp. 163-178.
H. Liu, J. Li and M. A. Chapman, “Automated Road Extraction from Satellite Imagery Using Hybrid Genetic Algorithms and Cluster Analysis”, Journal of Environmental Informatics, Vol.1, No.2, pp.40-47, 2003.
David Mulawa, “On-Orbit Geometric Calibration Of The Orbview-3 High Resolution Imaging Satellite”, In proceedings of the XXth International Society for Photogrammetry and Remote Sensing Congress (ISPRS), 2004.
S.Santhosh Baboo and M.Renuka Devi, “Geometric Correction in Recent High Resolution Satellite Imagery: A Case Study in Coimbatore, Tamil Nadu”, International Journal of Computer Applications, Vol. 14, No.1, pp.32-37, 2011.
Xuexia Chena, Lee Vierlinga, and Don Deering,“A simple and effective radiometric correction method to improve landscape change detection across sensors and across time”, Remote Sensing of Environment Journal, Vol. 98, No.1, pp.63–79, 2005.
Leonardo Paolini, Francisco Grings, Jose A. Sobrino, Juan C. Jime Nez Munoz and Haydee karszenbaum, “Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies”, International Journal of Remote Sensing, Vol. 27, No.4, pp.685–704, 2006.
T Schroeder, W Cohen, C Song, M Canty, and Z Yang, “Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon”, Remote Sensing of Environment Journal, Vol.103, No.1, pp. 685–704, 2006.
M. El Hajj , A. Begue , B. Lafrance , O. Hagolle , G. Dedieu , and M. Rumeau, “Relative radiometric normalization and atmospheric correction of a SPOT 5 time series”, Sensors Journal, Vol.8, pp.2774 – 2791, 2008.
Andrea Baraldi, Matteo Gironda, and Dario Simonetti, “Operational Two-Stage Stratified Topographic Correction of Spaceborne Multispectral Imagery Employing an Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier”, IEEE transactions on geoscience and remote sensing, Vol. 48, No. 1, pp.112-146, 2010.
Darren T. Janzen, Arthur L. Fredeen, and Roger D. Wheate, “Radiometric correction techniques and accuracy assessment for Landsat TM data in remote forested regions”, Can. J. Remote Sensing, Vol. 32, No. 5, pp. 330–340, 2006.
R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems”, Journal of Basic Engineering, Vol.82 (Series D), pp.35-45, 1960.
K. Levenberg, “A method for the solution of certain non-linear problems in least squares”,Quarterly Journal of Applied Mathmatics, Vol. II, No.22, pp.164--168,1944.
Priti Tyagi, Udhav Bhosle,”Radiometric correction of Multispectral Images using Radon Transform”, Journal of Indian Society of Remote Sensing, DOI 10.1007/s12524-013-0307-y, 2013