臺灣博碩士論文加值系統

Surface reflectance derived from optical satellite images is a fundamental and important material for remote sensing applications. The advanced atmospheric corrections have achieved a great progress in the removal of atmospheric effects by using complicated image- and physical-based models. Those algorithms are processed in each image data acquisition and using additional in-situ data information. However, the process of image-by-image atmospheric correction may cause bias errors for multitemporal images, which produce temporal inconsistencies and discontinuities over the surface reflectance of invariant ground objects. In this study, a surface reflectance bias removal is proposed for multispectral and multitemporal satellite images using a constrained harmonization. The constrained harmonization is applied by using pseudoinvariant features (PIFs) instead of an entire image. The PIFs are determined by using iterative reweighted multitemporal and multivariate alteration detection (IRMMAD), which considers a temporal weighting factor. With using PIFs, the constrained harmonization adopts two constraints, which are temporal and spectral consistency constraints. The temporal consistency is maintained with the temporal constraint while using the temporal and spectral constraints can maintain temporal consistency and spectral signatures consistency of surface reflectance during image harmonization. The multispectral and multitemporal surface reflectance bias removal is formulated as an optimization problem, and a least-squared linear system is solved for optimal harmonization. To test the robustness of the proposed method, six atmospheric corrections (AC) models were tested. Qualitative and quantitative analyses on the AC models of multitemporal images have been conducted, and the methods are evaluated using the measurements of temporal and spectral consistency qualities. The experimental results show that the surface reflectance biases can be significantly removed while the spectral signatures can be preserved. In addition, the proposed method outperforms related paired-wise harmonization and temporal-constrained harmonization methods in terms of consistency measurements.

Abstract iii
Acknowledgement v
Catalogue vi
List of Table viii
List of Figure ix
Chapter 1 Introduction 1
1.1. Histories of remote sensing 1
1.2. Atmospheric correction products 2
1.3. Motivation 7
Chapter 2 Literature review 13
2.1. Review of atmospheric corrections 13
2.2. Review of satellite image harmonization 20
2.3. Review of pseudo-invariant features (PIFS) 22
Chapter 3 Background 26
3.1 Principal component analysis (PCA) 26
3.2 CCA with MAD and IRMAD 28
3.2.1. CCA-MAD 28
3.2.2. CCA-IRMAD 34
3.3 GCCA-MAD 36
3.4 Paired-wise harmonization 42
Chapter 4 Methodology 45
4.1 System workflow 45
4.2 Multitemporal pseudo-invariant features (MPIFs) extraction 48
4.3 Constrained harmonization 58
4.4 Constraint’s analysis step 69
Chapter 5 Experimental Results and Discussions 71
5.1 Tested dataset 71
5.2 PIFs extraction results 74
5.3 Qualitative analyses of image harmonization 80
5.3.1. Constrained harmonization results 80
5.3.2. Evaluations of temporal-constrained and spectral-constrained harmonization 87
5.4 Quantitative analyses of image harmonization 94
Chapter 6 Conclusions 101
References 103

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