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系統識別號 U0026-3008201707441600
論文名稱(中文) 光學衛星影像擬恆定特徵物擷取使用多時期與多變數轉化偵測法
論文名稱(英文) Pseudo Invariant Features Selection for Optical Satellite Images Using Multitemporal and Multivariate Alteration Detection
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 105
學期 2
出版年 106
研究生(中文) 王志嘉
研究生(英文) Chih-Chia Wang
學號 P66041041
學位類別 碩士
語文別 英文
論文頁數 86頁
口試委員 指導教授-林昭宏
口試委員-饒見有
口試委員-張智安
中文關鍵字 輻射同態化  擬恆定特徵物  多變數轉化偵測法  廣義典型相關分析  多時期衛星影像 
英文關鍵字 radiometric normalization  pseudo invariant features  multivariate alteration detection  generalized canonical correlation analysis  multitemporal optical satellite images 
學科別分類
中文摘要 影像輻射同態化對於遙感探測使用多時期衛星影像而言是不可或缺的一項前處理步驟。衛星影像輻射同態化方法可依據所使用的資料分類成絕對及相對兩種形式。絕對輻射同態化的概念為透過若干資料的輔助,將衛星影像的灰階值轉換至所對應的地表反射率,然而此方法的問題在於有時並不能夠得到完整的輔助資料,例如影像拍攝當時的儀器校正參數、大氣改正模式或太陽入射角等。反之,相對輻射同態化可透過選定某一特定影像的灰階值或某一特定影像作為轉換基準,再將其它衛星影像的灰階值轉換至該基準上,以達成衛星影像的輻射同態化。雖然此方法並不需要影像拍照時的資訊,但在過程中,須選取影像上的不變像元作為轉換依據,換言之,相對輻射同態化成果好壞取決於所選之不變像元的品質優劣。不變像元(或稱為擬恆定特徵物)所代表的意義為在一段時間內某物體的反射率保持不變。在先前的研究當中,有研究人員利用多變數轉化偵測法來自動地挑選出兩張衛星影像中的不變像元。然而多變數轉化偵測法並不適合應用在兩張衛星影像中有劇烈地表覆蓋變動的情況,因會將原本該被歸類為變動像元的像元誤判為不變像元。因此迭代式多變數轉化偵測法透過迭代給權的方式來改善上述之缺點。儘管如此,不管是多變數轉化偵測法或是迭代式多變數轉化偵測法,皆只能應用於兩張衛星影像,這並不符合我們的需求。因此,本研究提出了加權式的廣義典型相關分析,用以同時選取多時期衛星影像中的不變像元,此方法可說是結合了廣義典型相關分析及迭代式多變數轉化偵測法之優點。最後再透過這些不變像元,將衛星影像的灰階值轉換至某一特定的參考基準,以達成多時期衛星影像之輻射同態化。本研究所使用的測試資料為SPOT-5,實驗結果顯示,本文方法能在多時期衛星影像中選取更正確的不變像元。
英文摘要 Radiometric normalization is a fundamental preprocessing for multitemporal optical satellite images. The methods of radiometric normalization can be classified into absolute and relative normalization based on the data required in the algorithm. Absolute normalization converts image digital numbers to Earth surface reflectance with the aids of sensor calibration data, atmospheric correction model, and sun angle, which are not always available. In contrast, relative normalization converts digital numbers of subject images to that of a selected reference image or to a common reference domain without the requirement of additional data. However, the accuracy of relative normalization depends on the quality of selected Pseudo Invariant Features (PIFs). PIFs represent the ground objects whose reflectance are constant during a period of time. In previous study, a method, called Multivariate Alteration Detection (MAD), was applied to statistically select no-changed pixels in bi-temporal satellite images. However, MAD is sensitive to significant land-cover changes such as cloud covers. Several clouds may be misclassified as PIFs in this method. For this reason, Iteratively Reweighted MAD (IR-MAD) was introduced to establish a better no-changed background using iterative scheme. Nonetheless, both MAD and IR-MAD compute linear combinations which are suitable for bi-temporal images only, and are not applicable for multitemporal images with more than two images. In this study, a novel method called Weighted Generalized Canonical Correlation Analysis (WGCCA) is proposed for the selection of high-quality PIFs for multitemporal and multispectral images. The proposed method computes correlation coefficients for not only multivariable data but also multitemporal data. Specifically, the method integrates the strengths of Generalized Canonical Correlation Analysis (GCCA) and IR-MAD, and PIFs are extracted simultaneously from a sequence of satellite images, which leads to a consistent PIFs extraction. Furthermore, when the high-quality PIFs are determined by the proposed method, the digital numbers of PIFs from multitemporal images are transformed into a predefined radiometric reference level. With this approach, the radiometric resolution of multitemporal images can be preserved. In experiments, SPOT-5 imagery was tested. Compared with Canonical Correlation Analysis (CCA) which is used in MAD and IR-MAD, the proposed method can discriminate no-changed pixels from changed more accurately.
論文目次 摘要 I
Abstract III
致謝 V
CATALOG VI
List of Tables VIII
List of Figures VIII
Chapter1 Introduction 1
Chapter2 Background 7
2.1 Multivariate Alteration Detection 7
2.2 Iteratively Reweighted Multivariate Alteration Detection 11
2.3 Review of radiometric normalization 12
Chapter3 Methodology 15
3.1 System Workflow 15
3.2 Image Alignment 17
3.3 Generalized Canonical Correlation Analysis 17
3.4 Weighted Generalized Canonical Correlation Analysis 25
3.4.1 Design of Connected Network 27
3.4.2 Objective Function 28
3.4.3 Iteratively Reweighted Scheme 28
3.5 PIFs Determination 33
3.6 Radiometric Normalization 34
Chapter4 Experimental Results 36
4.1 SPOT-5 Data 36
4.2 Study area 37
4.3 Experimental Results 37
4.3.1 Comparisons of Initial Weights in IR-MAD 38
4.3.2 Bitemporal PIFs Extraction 43
4.3.3 PIFs Determination for Each Image 49
4.3.4 Radiometric Normalization 54
4.4 Quantitative assessment 61
Chapter5 Conclusions and Future Works 67
References 69
Appendix 73
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