這篇論文提出了一個針對遙測衛星影像，經由傳輸過程造成損壞的多光譜影像，利用全色態PAN結合多光譜B1, B2, B3影像產生預測B ̃_4影像進行修補，如果個別使用多光譜B1, B2, B3影像修補B4，但是這樣雖然各波段影像結構上相似，但各波段對四種典型地物（雪、沙漠、濕地、植被）的反射光譜差異頗大，會造成修補後影像看起來不自然，而提出的演算法，則是使用預測B ̃_4影像作為參考影像。在全色態的波段涵蓋多光譜大部分波段，所以全色態影像包含多光譜影像的資訊，藉由結合未損壞多光譜影像，並基於這些多張影像資訊，產生預測B ̃_4影像，藉由預測B ̃_4影像作為參考影像來復原多光譜影像B4。實驗結果顯示，我們的演算法可以穩定地增強現有演算法，使影像的品質更好，而且讓整體影像看起來自然且可以清楚地呈現出更多原有多光譜波段影像B4細節。

This thesis aims at the multi-spectral image error caused during the transmission of the remote sensing satellite image to combine the panchromatic image with multi-spectral B1, B2, B3 images as a way to generate predicted B ̃_4 image for restoration. In case the multi-spectral B1, B2, B3 images are used respectively to patch the B4 image, though the structure of the band image is similar, the difference of the reflective spectrum on four typical land objects (snow, desert, wetland, vegetation) is significant that the image looked unnatural so the existing algorithm is applied to improve the current loss. The panchromatic band covers most of the multi-spectral bands that the multi-spectral image data is included in the PAN image, and through the fusion of the non-damaged multi-spectral images and the data of these images to generate the B ̃_4 image prediction as a reference image to restore the multi-spectral image B4. The experimental results indicated that the algorithm may stably strengthen up the existing algorithm to better the image quality and allow the overall image look much natural, also, to present more details contained in the multi-spectral band image B4 clearly.

[1] [Online] Avaiab le: http://www.crisp.nus.edu.sg/~research/tutorial/intro.htm
[2] R. E. W. Rafael C . Gonzalez, Digital Image Processing 3rd Edition. PEARSON, 2007
[3] [Online]. Available: http://www.cpc.unc.edu/
[4] S. C. Tai, Yun-Wei Chang, ”Modified CCDS Compression Algorithm for Single Error
Bit Correction,” Master’s thesis, National Cheng Kung University, Tainan, Taiwan,
R.O.C.,2014
[5] [Online] Avaiab le:http://www.nspo.narl.org.tw/tw/
[6] M. Sonka, V. Hlavac, and R. Boyle, Image Porcessing, Analysis, and Machine Vision,
PWS Pubishing, pp. 121-123, 2006.
[7] [Online]Avaiable:https://cg2010studio.wordpress.com/2012/02/19/scaling-algorithm
[8] Medha V. Wyawahare, Dr. Pradeep M. Patil, and Hemant K. Abhyankar, “Image
Registration Techniques: An overview” International Journal of Signal Processing,
Image Processing and Pattern Recognition ,Vol. 2, No.3, September 2009
[9] Feng Zhao, Qingming Huang, Wen Gao, “Image Matching By Normalized
Cross-Correlation”,Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China Graduate School of the Chinese Academy of Sciences, Beijing, China
[10] X. Zhu, F. Gao, D. Liu, and J. Chen, ”A modified neighborhood similar pixel
interpolator approach for removing thick clouds in landsat images,” Geoscience and Remote Sensing Letters, IEEE, vol. 9, no. 3, pp. 521-525, May 2012.
[11] S. C. Tai, Chiean-Yen Chao, “A Restoration Algorithm for Bit-Error-Caused Damages
in CCSDS Satellite Images, ” Master’s thesis, National Cheng Kung University,
Tainan, Taiwan, R.O.C.,2014
[12] S. C. Tai, Yu-En Huang, “Restoration for Damaged CCDS Images by using Cross Band Information,” Master’s thesis, National Cheng Kung University, Tainan, Taiwan, R.O.C.,2014