Robust Radiometric Normalization of the Near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis — Oak Academic Publishing
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Robust Radiometric Normalization of the Near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis
Water Resources Engineering Department, Faculty of Engineering, Al-Qasim Green University, Al-Qasim, Iraq
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Faculty of Engineering, Geospatial Information Science Research Centre, Department of Civil Engineering, University Putra Malaysia, Selangor, Malaysia
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National Space Agency Malaysia (ANGKASA), Kementerian Sains, Teknologi dan Inovasi, Pusat Angkasa Negara, Banting, Malaysia
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Department of Civil Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden
1 Water Resources Engineering Department, Faculty of Engineering, Al-Qasim Green University, Al-Qasim, Iraq
2 Faculty of Engineering, Geospatial Information Science Research Centre, Department of Civil Engineering, University Putra Malaysia, Selangor, Malaysia
3 National Space Agency Malaysia (ANGKASA), Kementerian Sains, Teknologi dan Inovasi, Pusat Angkasa Negara, Banting, Malaysia
4 Department of Civil Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden
Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ ban ds, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands , 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.
KeywordsRelative Radiometric NormalizationScale Invariant Feature TransformAutomatically Extraction Control PointsSum of Absolute Difference
Hasab, H.A., Jawad, H.A., Dibs, H., Hussain, H.M. and Al-Ansari, N. (2020) Evaluation of Water Quality Parameters in Marshes Zone Southern of Iraq Based on Remote Sensing and GIS Techniques. Water, Air, & Soil Pollution, 231, Article No. 183. https://doi.org/10.1007/s11270-020-04531-z
Dibs, H., Mansor, S., Ahmad, N., Pradhan, B. and Al-Ansari, N. (2020) Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images. Journal of Civil Engineering and Architecture, 14, 339-350. https://doi.org/10.17265/1934-7359/2020.06.005
Dibs, H., Hasab, H.A., Al-Rifaie, J.K. and Al-Ansari, N. (2020) An Optimal Approach for Land-Use/Land-Cover Mapping by Integration and Fusion of Multispectral Landsat OLI Images: Case Study in Baghdad, Iraq. Water, Air, & Soil Pollution, 231, Article No. 488. https://doi.org/10.1007/s11270-020-04846-x
Dibs, H., Hasab, H.A., Jaber, H.S. and Al-Ansari, N. (2021) Automatic Feature Extraction and Matching Modelling for Highly Noise Near-Equatorial Satellite Images. Innovative Infrastructure Solutions, 7, Article No. 2. https://doi.org/10.1007/s41062-021-00598-7
Richards, J.A. (2013) Remote Sensing Digital Image Analysis. Springer-vertag, Berlin. https://doi.org/10.1007/978-3-642-30062-2
Hasab, H.A., Dibs, H., Dawood, A.S., Hadi, W.H., Hussain, H.M. and Al-Ansari, N. (2020) Monitoring and Assessment of Salinity and Chemicals in Agricultural Lands by a Remote Sensing Technique and Soil Moisture with Chemical Index Models. Geosciences, 10, 207. https://doi.org/10.3390/geosciences10060207
Seo, D.K., Kim, Y.H., Eo, Y.D., Park, W.Y. and Park, H.C. (2017) Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection. Remote Sensing, 9, Article No. 1163. https://doi.org/10.3390/rs9111163
Dibs, H., Mansor, S., Ahmad, N. and Pradhan, B. (2014) Registration Model for Near-Equatorial Earth Observation Satellite Images Using Automatic Extraction of Control Points. International Symposium and Exhibition on Geoinformation, Volume 3, 2184-2200. https://doi.org/10.1080/01431161.2015.1034891
Zhang, L., Wu, C. and Du, B. (2014) Automatic Radiometric Normalization for Multitemporal Remote Sensing Imagery with Iterative Slow Feature Analysis. IEEE Transactions on Geoscience and Remote Sensing, 52, 6141-6155. https://doi.org/10.1109/TGRS.2013.2295263
Dibs, H. (2018) Comparison of Derived Indices and Unsupervised Classification for AL-Razaza Lake Dehydration Extent Using Multi-Temporal Satellite Data and Remote Sensing Analysis. Journal of Engineering and Applied Science, 13, 1-8.
Dibs, H., Al-Hedny, S. and Karkoosh, H.A. (2018) Extracting Detailed Buildings 3D Model with Using High Resolution Satellite Imagery by Remote Sensing and GIS Analysis; Al-Qasim Green University a Case Study. International Journal of Civil Engineering and Technology, 9, 1097-1108.
Moghimi, A., Mohammadzadeh, A., Celik, T. and Amani, M. (2020) A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images. IEEE Transactions on Geoscience and Remote Sensing, 59, 2503-2519. https://doi.org/10.1109/TGRS.2020.2995394
Liu, K., Ke, T., Tao, P., He, J., Xi, K. and Yang, K. (2020) Robust Radiometric Normalization of Multitemporal Satellite Images via Block Adjustment without Master Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6029-6043. https://doi.org/10.1109/JSTARS.2020.3028062
Jason, T.C. and John, P.K. (2009) Mis-Registration Impacts on Hyperspectral Target Detection. Journal of Applied Remote Sensing, 3, Article ID: 033513.
Dibs, H., Mansor, S., Ahmad, N. and Pradhan, B. (2015) Band-to-Band Registration Model for Near-Equatorial Earth Observation Satellite Images with the Use of Automatic Control Point Extraction. International Journal of Remote Sensing, 36, 2184-2200. https://doi.org/10.1080/01431161.2015.1034891
Dibs, H. and Hussain, T.H. (2018) Estimation and Mapping the Rubber Trees Growth Distribution Using Multi Sensor Imagery with Remote Sensing and GIS Analysis. Journal of University of Babylon for Pure and Applied Sciences, 26, 109-123.
Schott, J.R., Salvaggio, C. and Volchok, W.J. (1988) Radiometric Scene Normalization Using Pseudo Invariant Features. Remote Sensing of Environment, 26, 1-16. https://doi.org/10.1016/0034-4257(88)90116-2
Hall, F.G., Strebel, D.E., Nickeson, J.E. and Goetz, S.J. (1991) Radiometric Rectification: Toward a Common Radiometric Response among Multidate, Multisensor Images. Remote Sensing of Environment, 35, 11-27. https://doi.org/10.1016/0034-4257(91)90062-B
Allan, A.N. (2011) Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations. IEEE Transactions on Image Processing, 20, 612-624. https://doi.org/10.1109/TIP.2010.2076296
Dibs, H., Mansor, S., Ahmadb, N. and Al-Ansari, N. (2020) Simulate New Near Equatorial Satellite System by a Novel Multi-Fields and Purposes Remote Sensing Goniometer. Engineering, 12, 325-346. https://doi.org/10.4236/eng.2020.126026
Morton, J.C. Allan, A.N. (2008) Automatic Radiometric Normalization of Multitemporal Satellite Imagery with the Iteratively Re-Weighted MAD Transformation. Remote Sensing of Environment, 112, 1025-1036. https://doi.org/10.1016/j.rse.2007.07.013
Carlos, J., Broncano, M., Carlos, P.R., Rubén, G.C., Andrés, C.S., Brown, M. and Lowe, D.G. (2010) Invariant Features from Interest Point Groups. Machine Vision Conference, Cardiff, 2-5 September 2002, 656-665.
Fahad, K.H., Hussein, S. and Dibs, H. (2020) Spatial-Temporal Analysis of Land Use and Land Cover Change Detection Using Remote Sensing and GIS Techniques. IOP Conference Series: Materials Science and Engineering, 671, 12-46. https://doi.org/10.1088/1757-899X/671/1/012046
Hashim, F., Dibs, H. and Jaber, H.S. (2021) Applying Support Vector Machine Algorithm on Multispectral Remotely Sensed Satellite Image for Geospatial Analysis. Journal of Physics: Conference Series, 1963, Article ID: 012110. https://doi.org/10.1088/1742-6596/1963/1/012110
Dibs, H. and Al-Hedny, S. (2019) Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques. International Journal of Civil Engineering and Technology, 10, 143-154.
Baisantry, M., Negi, D. and Manocha, O. (2012) Automatic Relative Radiometric Normalization for Change Detection of Satellite Imagery. International Journal on Information Technology, 2, 116-124.
Connell, J., Connolly, J., Vermote, E.F. and Holden, N.M. (2013) Radiometric Normalization for Change Detection in Peatlands: A Modified Temporal Invariant Cluster Approach. International Journal of Remote Sensing, 34, 2905-2924. https://doi.org/10.1080/01431161.2012.752886
Hashim, F., Dibs, H. and Jaber, H.S. (2022) Adopting Gram-Schmidt and Brovey Methods for Estimating Land Use and Land Cover Using Remote Sensing and Satellite Images. Nature Environment and Pollution Technology, 21, 867-881. https://doi.org/10.46488/NEPT.2022.v21i02.050
Sadeghi, V., Ebadi, H. and Ahmadi, F.F. (2013) A New Model for Automatic Normalization of Multi-Temporal Satellite Images Using Artificial Neural Network and Mathematical Methods. Applied Mathematical Modelling 37, 6437-6445. https://doi.org/10.1016/j.apm.2013.01.006
Nazeer, M., Wong, M.S. and Nichol, J.E. (2017) A New Approach for the Estimation of Phytoplankton Cell Counts Associated with Algal Blooms. Science of the Total Environment, 5, 125-138. https://doi.org/10.1016/j.scitotenv.2017.02.182
Dibs, H., Idrees, M.O., Saeidi, V. and Mansor, S. (2016) Automatic Keypoints Extraction from UAV Image with Refine and Improved Scale Invariant Features Transform (RI-SIFT). International Journal of Geoinformatics, 12, 51-58.
Huang, L.T., Jiao, W.L., Long, T.F. and Kang, C.L. (2020) A Radiometric Normalization Method of Controlling No-Changed Set (CNCS) for Diverse Landcover Using Multi-Sensor Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 863-870. https://doi.org/10.5194/isprs-archives-XLII-3-W10-863-2020
Dibs, H., Hasab, H.A., Mahmoud, A.S. and Al-Ansari, N. (2021) Fusion Methods and Multi-Classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis. Geotechnical and Geological Engineering, 39, 5825-5842. https://doi.org/10.1007/s10706-021-01869-x
Moghimi, A., Sarmadian, A., Mohammadzadeh, A., Celik, T., Amani, M. and Kusetogullari, H. (2021) Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features. IEEE Transactions on Geoscience and Remote Sensing, 60, Article ID: 5400820. https://doi.org/10.1109/TGRS.2021.3063151
Yuan, D. and Elvidge, C.D. (1996) Comparison of Relative Radiometric Normalization Techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 51, 117-126. https://doi.org/10.1016/0924-2716(96)00018-4
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P. and Macomber, S.A. (2001) Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects. Remote Sensing of Environment, 75, 230-244. https://doi.org/10.1016/S0034-4257(00)00169-3
Canty, M.J., Nielsen, A.A. and Schmidt, M. (2004) Automatic Radiometric Normalization of Multi-Temporal Satellite Imagery. Remote Sensing of Environment, 91, 441-451. https://doi.org/10.1016/j.rse.2003.10.024
Qian, D., Nareenart, R., Aksuntorn, A.O. and Lori, M.B. (2008) Automatic Registration and Mosaicking for Airborne Multispectral Image Sequences. Photogrammetric Engineering & Remote Sensing, 74, 169-181. https://doi.org/10.14358/PERS.74.2.169
Loon, H., Thomas, W. and fltzHugh, A. (2000) Standardized Radiometric Normalization Method for Change Detection Using Remotely Sensed Imagery. Photograrnmetric Engineering & Remote Sensing, 66, 173-181.
Gonzalez, R.C. and Woods, R.E. (2008) Digital Image Processing. 3rd Edition, Pearson Prentice, London.
Chureesampant, K. and Susaki, J. (2012) Automatic Unsupervised Change Detection Using Multi-Temporal Polarimetric SAR Data. IEEE International Geoscience and Remote Sensing Symposium, 1, 6192-6195. https://doi.org/10.1109/IGARSS.2012.6352671
Chureesampant, K. and Susaki, J. (2014) Automatic GCP Extraction of Fully. Geoscience and Remote Sensing, IEEE Transactions, 52, 137-148. https://doi.org/10.1109/TGRS.2012.2236890