遙感探測應用中，輻射同態化是基本且必要的處理過程。衛星影像若沒預處理，會因感測器在不同太陽入射角、衛星幾何與大氣條件對所造成的不確定性而無法直接使用。由於每日精確的大氣模型與地面觀測值不容易獲取，因此本研究採用相對輻射同態化縮小多時期衛星影像輻射間的差異性。相對輻射同態化品質好壞的關鍵，在於倆倆影像間所萃取出之擬恆定特徵物，以及透過它們所推算之回歸線的參數。先前的研究多採用將每個波段獨立同態化，因此可能造成波譜的不連續。本研究中提出帶約制條件之回歸方程式求解同態化的參數，可在相對輻射同態化過程中同時維持原本影像之波譜簽名。此外，參考影像是由倆倆影像中的關係計算獲得，而非直接選擇既有的參考影像，減少輻射同態化之不連續性。本研究使用的測試資料為多對Landsat-8衛星影像，並透過頻譜距離與相似度作為品質與量化實驗評估。實驗證明本研究所提出的方法在頻譜簽名的一致性與同態化之優越性。

Radiometric normalization is a fundamental and important pre-processing step because the at-sensor reflectance suffers uncertainties caused different sun angle observation and atmospheric conditions. In case that the atmospheric model and ground measurements are not available, relative normalization is an alternative method that minimizes the radiometric level differences among images without the requirements of additional atmospheric and ground information. The key to relative normalization is the selection of pseudo-invariant features (PIFs) from bi-temporal images and the regression of selected PIFs. PIFs is a group of objects/pixels whose reflectance values are constant over the period of image acquisitions. Previous studies on relative radiometric normalization determine transformation coefficients using band-by-band regression of selected PIFs. The studies can obtain satisfied normalization result for each band; however, they do not fully consider the spectral inconsistency problem caused by individual band regression. To alleviate this problem, a constrained regression is proposed, which enforces pixel spectral signature to be as consistent as possible during radiometric normalization while preserving band regression quality. Qualitative and quantitative analysis of image sequences acquired by Landsat 8 were conducted to evaluate the proposed method using the measurements of spectral distance and spectral similarity. The experimental results demonstrate the superiority of the proposed method to related methods in terms of spectral consistency.

Aduah, M. S. (2007) Multitemporal Remote Sensing for Mapping and Monitoring Floods: An Approach Towards Validation of the KAFRIBA Model, Kafue Flats, Zambia. ITC.
Ban, Y. (2016) Multitemporal Remote Sensing. Cham, Switzerland: Springers.
Belward, A. S. and Skøien, J. O. (2015) ‘Who launched what , when and why; trends in global land-cover observation capacity from civilian earth observation satellites’, ISPRS Journal of Photogrammetry and Remote Sensing. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS), 103, pp. 115–128.
Canty, M. J. and Nielsen, A. A. (2004) ‘Automatic Radiometric Normalization of Multitemporal Satellite Imagery’, Remote Sensing of Environment, 91, pp. 441–451.
Canty, M. J. and Nielsen, A. A. (2008) ‘Automatic Radiometric Normalization of Multitemporal Satellite Imagery with the Iteratively Re-weighted MAD Transformation’, Remote Sensing of Environment, 112, pp. 1025–1036.
Carvalho, O. A. D. J., Guimarães, R. F., Silva, N. C., Gillespie, A. R., Gomes, R. A. T., Silva, C. R. and Carvalho, A. P. F. De (2013) ‘Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot’, Remote Sensing, 5, pp. 2763–2794.
Caselles, V. and Lopez Garcia, M. J. (1989) ‘Alternative Simple Approach to Estimate Atmosphere Correction in Multitemporal Studies’, International Journal of Remote Sensing, 10, pp. 1127–1134.
Clement, M. A. (2017) ‘Multi-temporal Synthetic Aperture Radar Flood Mapping using Change Detection’, Journal of Flood Risk Management, pp. 1–17.
Conel, J. E. (1990) ‘Detrmination of Surface Reflectance and Estimates of Atmospheric Optical Depth and Single Scattering Albedo from Landsat Thematic Mapper Data’, Earth Resources and Remote Sensing, 11, pp. 783–828.
Coppin, P. R. and Bauer, M. E. (1996) ‘Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery Digital Change Detection in Temperate Forests’, Remote Sensing Reviews, 13, pp. 207–234.
Davis, G. (2007) ‘History of the NOAA Satellite Program’, Journal of Applied Remote Sensing, 1(January 2007), pp. 1–18.
Dowman, I., Jacobsen, K., Konecny, G. and Sandau, R. (2012) High Resolution Optical Satellite Imagery.
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, pp. 123–134.
Fraser, R. S., Ferrare, R. A., Kaufman, Y. J., Mattoo, S. and Fraser, R. S. (1989) ‘Algorithm for Atmospheric Corrections of Aircraft and Satellite Imagery’, NASA Technical Memorandum, p. 106 pp.
Hall, F. . G., Strebel, D. F., Nickeson, J. E. and Goetz, S. J. (1991) ‘Radiometric Rectification : Toward a Common Radiometric Response Among Multidate, Multisensor Images’, Remote Sensing of Environment, 35, pp. 11–27.
Hotelling, B. Y. H. (1936) ‘Relations between Two Sets of Varieties’, Biometrika, 28, pp. 321–377.
Jianya, G., Haigang, S., Guorui, M. and Qiming, Z. (2008) ‘A Review of Multi-temporal Remote Sensing Data Change Detection’, Remote Sensing and Spatial Information Sciences, 37, pp. 757–762.
Kaufman, Y. J. (1987) ‘Atmospheric Effect on Spectral Siganture - Measurements’, Adv. Space Res., 7, pp. 203–206.
Kendall, M. and Stuart, A. (1979) The Advanced Theory of Statistics. London: Charles Griffin.
Light, D. L. (1990) ‘Characteristics of Remote Sensors for Mapping and Earth Science Applications’, 56, pp. 1613–1623.
Lin, C., Lin, B., Lee, K. and Chen, Y. (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. doi: 10.1016/j.isprsjprs.2015.05.003.
Lu, Z., Mingsheng, L., Yan, W., Lijun, L. U. and Yong, W. (2004) ‘Robust Approach to the MAD Change Detection Method’, Proceedings of SPIE, 5574, pp. 184–193.
Moraniec, M. (2011) ‘Landsat : An Earth-Observing Trailblazer’.
Nelson, R. F. (1983) ‘Detecting Forest Canopy Change due to Insect Activity using Landsat MSS’, Earth Resources and Remote Sensing, 49, pp. 1303–1314.
Nielsen, A. A. (2007) ‘The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data’, IEEE Transaction on Image Processing, 16, pp. 463–478.
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’, Remote Sensing of Environment. Elsevier B.V., 64, pp. 1–19.
Paolini, L., Grings, F., Sobrino, J. A., Muñoz, J. C. J., Karszenbaum, H., Paolini, L., Grings, F., Sobrino, J. A. and Jiménez, J. C. (2006) ‘Radiometric Correction Effects in Landsat Multi-date / Multi‐sensor Change Detection Studies’, International Journal of Remote Sensing, 27, pp. 685–704.
Refice, A., Addabbo, A. D., Lovergine, F. P., Tijani, K., Morea, A., Nutricato, R., Bovenga, F. and Nitti, D. O. (2018) ‘Monitoring Flood Extent and Area Through Multisensor , Multi-temporal Remote Sensing : The Strymonas ( Greece ) River Flood’, Flood Monitoring, pp. 101–113.
Rustamov, R. B., Salahova, S. E., Zeynalova, M. H. and Hasanova, S. N. (2012) Earth Observation – Space Technology.
Schott, J. R., Salvaggio, C. and Volchok, W. J. (1988) ‘Radiometric Scene Normalization Using Pseudoinvariant Features’, Remote Sensing of Environment, 16, pp. 1–16.
Singh, A. (1986) ‘Change Detection in the Tropical Forest Environment of Northeastern India using Landsat’, Remote Sensing and Tropical Land Management, pp. 237–254.
Singh, A. (1989) ‘Digital Change Detection Techniques using Remotely-sensed Data’, International Journal of Remote Sensing, 10, pp. 989–1003.
Tucker, C. J. and Townshend, J. R. G. (2000) ‘Strategies for Monitoring Tropical Deforestation using Satellite Data’, International Journal of Remote Sensing, 21(December), pp. 1461–1471.
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, pp. 967–980.
Zhang, L., Wu, C. and Du, B. (2014) ‘Automatic Radiometric Normalization for Multitemporal Remote Sensing Imagery With Iterative Slow Feature Analysis’, IEEE Transaction on Geoscience and Remote Sensing, 52, pp. 6141–6155.