||跨感測器相對輻射校正演算法使用於 Landsat 7 ETM+ 和 Landsat 8 OLI 衛星影像
||Cross-sensor Relative Radiometric Normalization for Multi-temporal Landsat 7 ETM+ and Landsat 8 OLI Imagery
||Department of Geomatics
||Lino Garda Denaro
口試委員-Lalu Muhamad Jaelani
Cross-sensor relative radiometric normalization
pseudo-invariant feature selection
multivariate alteration detection
kernel canonical correlation analysis.
Processing of multi-temporal satellite images usually suffer uncertainties caused by differences in illumination and observation angles, and variation in atmospheric conditions. Moreover, satellite images acquired from different sensors contain not only aforementioned uncertainties but disparate relative spectral response. Since radiometric calibration and correction of satellite images is difficult without the ground measurements at the time of data acquisition, this study addresses on relative radiometric normalization (RRN) to minimize the radiometric differences among images caused by atmospheric inconsistency and even spectral band inconsistency during the data acquisition. The key to a successful RRN is the selection of Pseudo-invariant features (PIFs) between bi-temporal images. To select PIFs, multivariate alteration detection (MAD) algorithm is adopted with kernel canonical correlations analysis (KCCA) instead of canonical correlation analysis (CCA). Compared with CCA that assumes the relationship between the at-sensor radiances of bi-temporal image is homogeneous, KCCA that assumes the relationship between sensor radiance is heterogeneous can obtain more appropriate PIFs for cross-sensor images. Qualitative and quantitative analyses of bi-temporal images acquired by Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and Landsat-8 Operational and Imager (OLI) are conducted to evaluate the proposed method. The experimental results demonstrate the superiority of the proposed KCCA-based MAD to the CCA-based MAD in terms of radiometric consistency, particularly for images containing many cloud covers.
List of Table vi
List of Figure vii
Chapter 1 Introduction 1
Chapter 2 Background 6
2.1 Review of radiometric normalization 6
A. Ordinary least squares (OLS) regression 9
B. Orthogonal least square regression 11
2.2 Review of pseudo-invariant features (PIFs) selection 12
Chapter 3 Methodology 17
3.1 System workflow 17
3.2 CCA-based MAD 21
3.3 KCCA-based MAD 25
Chapter 4 Experimental Results and Discussion 36
4.1 Landsat data 36
4.2 Experimental results 38
4.3 Evaluation 44
Chapter 5 Conclusion and Limitation 53
Bach, F. R. and Jordan, M. I. (2002) ‘Kernel Independent Component Analysis’, Journal of Machine Learning Research, 3, pp. 1–48.
Biessmann, F. (2010) ‘Temporal Kernel CCA and its Application in Multimodal Neuronal Data Analysis’, Machine Learning, 79, pp. 5–27.
Canty, M. J. and Nielsen, A. A. (2008) ‘Automatic radiometric normalization of multitemporal satellite imagery’, Remote Sensing of Environment, 112(3), pp. 1025–1036.
Du, Y., Teillet, P. M. and Cihlar, J. (2002) ‘Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection’, Remote Sensing of Environment, 82(1), pp. 123–134.
El-Askary, H., Abd El-Mawla, S. H., Li, J., El-Hattab, M. M., El-Raey, M., (2014) ‘Change detection of coral reef habitat using Landsat-5 TM, Landsat 7 ETM+ and Landsat 8 OLI data in the Red Sea (Hurghada, Egypt)’, International Journal of Remote Sensing, 35(6), pp. 2327–2346.
Hall, F. G., Strebel, D. E., Nickeson, J. E., Goetz, S. J., (1991) ‘Radiometric rectification: Toward a common radiometric response among multidate, multisensor images’, Remote Sensing of Environment, 35(1), pp. 11–27.
Hardoon, D., Szedmak, S. and Shawe-Taylor, J. (2004) ‘Canonical Correlation Analysis: An Overview with Application to Learning Methods’, Neural Computation, 16(12), pp. 2639–2664.
Hotelling, H. (1985) ‘Relations Between Two Sets of Variates’, in. Columbia University.
Kuss, M. and Graepel, T. (2003) ‘The Geometry of Kernel Canonical Correlation Analysis’, Biological Cybernetics, (108), pp. 0–10.
Li, P., Jiang, L. and Feng, Z. (2013) ‘Cross-comparison of vegetation indices derived from landsat-7 enhanced thematic mapper plus (ETM+) and landsat-8 operational land imager (OLI) sensors’, Remote Sensing, 6(1), pp. 310–329.
Lin, Chao-Hung, Lin, Bo-Yi, Lee, Kuan-Yi, Chen, Yi-Chen, (2015) ‘Radiometric normalization and cloud detection of optical satellite images using invariant pixels’, ISPRS Journal of Photogrammetry and Remote Sensing. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS), 106, pp. 107–117.
Markham, B. L. and Helder, D. L. (2012) ‘Forty-year calibrated record of earth-reflected radiance from Landsat: A review’, Remote Sensing of Environment. Elsevier B.V., 122, pp. 30–40.
Mishra, Nischal, Haque, Md Obaidul, Leigh, Larry, Aaron, David, Helder, Dennis, Markham, Brian, (2014) ‘Radiometric cross-calibration of landsat 8 Operational Land Imager (OLI) and landsat 7 enhanced thematic mapper plus (ETM+)’, Remote Sensing, 6(12), pp. 12619–12638.
Nielsen, A. A., Conradsen, K. and Simpson, J. J. (1998) ‘Multivariate alteration detection MAD and MAF postprocessing in multispectral bitemporal image data New approaches to change detection studies.pdf’, Remote Sensing of Environment, 4257(97), pp. 1–19.
Olthof, Ian, Pouliot, Darren, Fernandes, Richard, Latifovic, Rasim (2005) ‘Landsat-7 ETM+ radiometric normalization comparison for northern mapping applications’, Remote Sensing of Environment, 95(3), pp. 388–398.
Rice, J. a (1995) Mathematical Statistics and Data Analysis, Higher Education.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., Egorov, A., (2016) ‘Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity’, Remote Sensing of Environment. The Authors, 185, pp. 57–70.
Sun, KaiMin, HaiGang Sui, Li, DeRen, Chuan Xu, (2011) ‘A new relative radiometric consistency processing method for change detection based on wavelet transform and a low-pass filter’, Science China Technological Sciences, 7, pp. 3–10.
Teillet, P. M. (1986) ‘Topographic correction’, in, pp. 1637–1651.
Tenenhaus, A., Philippe, C. and Frouin, V. (2015) ‘Kernel Generalized Canonical Correlation Analysis’, Computational Statistics and Data Analysis. Elsevier B.V., 90, pp. 114–131.
Volpi, M., Camps-Valls, G. and Tuia, D. (2015) ‘Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis’, ISPRS Journal of Photogrammetry and Remote Sensing, 107 (March 2016), pp. 50–63.
Yang, X. and Lo, C. P. (2000) ‘Relative radiometric normalization performance for change detection from multi-date satellite images’, Photogrammetric Engineering and Remote Sensing, 66(August), pp. 967–980.
Yuan, D. and Elvidge, C. D. (1996) ‘Comparison of relative radiometric normalization techniques’, ISPRS Journal of Photogrammetry and Remote Sensing, 51(3), pp. 117–126.