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系統識別號 U0026-0602201815313600
論文名稱(中文) 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
論文名稱(英文) Cross-sensor Relative Radiometric Normalization for Multi-temporal Landsat 7 ETM+ and Landsat 8 OLI Imagery
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 106
學期 1
出版年 107
研究生(中文) 李諾
研究生(英文) Lino Garda Denaro
學號 P66057076
學位類別 碩士
語文別 英文
論文頁數 56頁
口試委員 指導教授-林昭宏
召集委員-蔡富安
口試委員-王驥魁
口試委員-Lalu Muhamad Jaelani
中文關鍵字 none 
英文關鍵字 Cross-sensor relative radiometric normalization  pseudo-invariant feature selection  multivariate alteration detection  kernel canonical correlation analysis. 
學科別分類
中文摘要 none
英文摘要 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.
論文目次 Abstract i
Acknowledgement iii
Catalog iv
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
References 56
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