利用比爾─朗伯定律及凸幾何分析進行近岸淺海水深與棲地底質含量估計

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林亭勻(Lin Ting-Yun) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

利用比爾─朗伯定律及凸幾何分析進行近岸淺海水深與棲地底質含量估計
(Bathymetry and Habitat Abundance Estimation with Beer-Lambert Law and Convex Analysis in Coastal Area)

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摘要(中)

海洋水深探測不論在船隻航行、水域經營或是海洋生態研究上都扮演了一個重要的角色。隨著衛星科技的進步，被動式的衛星水深探測法(SDB, Satellite Derived Bathymetry)漸漸取代船載回波測深儀或空載光達等主動式水深遙測方法，運用衛星影像大範圍大尺度的調查可有效的改善主動式遙測法因成本高昂、耗時費力且易受到氣候所影響的缺點，而傳統船載回波測深更因地形船載回波測深航行安全性而導致近岸淺海水深資料難以取得。過去利用光學衛星所進行的水深反演演算法大多是建立於比爾─朗伯定律上的物理法則及經驗法則，例如比率演算法(ratio algorithm)、查表法(LUT, Look-Up Table)和透過類神經網路的經驗學習法，不過這些方法皆未考慮海床底質的組成複雜，而是將各個像素都視為只含有單一物質的純像素(pure pixel)。
由於衛星影像的空間解析度，影像中像素大多包含多種不同底質，而成為混合像素(mixed pixel)。本研究結合光譜解混(spectral unmixing)的技術與比爾─朗伯定律，由幾何的角度探討水深對混合像素光譜的影響。光譜解混主要利用線性模型(linear model)，其假設一個像素的光譜是所包含純物質光譜的線性混合，故在多維度空間中該像素皆會位於以純物質為頂點的凸多面體之中。若考慮光譜衰減與水深呈指數相關，又因各光譜波段的衰減係數皆不同且獨立，此多面體將隨水深增加而旋轉並縮小。本研究期望能透過結合光譜解混及比爾─朗伯定律提出新的近岸淺海水深測量演算法，以提高水深估計的準確度，並同時估計棲地底質物質光譜及其含量分布，此外，光譜解混技術使此演算法在較低空間解析度的衛星影像中也可進行水深估算。本研究經由4個模擬實驗觀察此理論在不同條件下的可行性，經由實驗可知道此方法須要有足夠多的像素且各物質皆有含量0.9的高純度像素且分布在水深近乎於0的區域才能得到良好的水深估算。

摘要(英)

Bathymetry estimation plays an essential role in navigation, area management, and marine ecological research. With the advancement of satellite technology, passive satellite derived bathymetry (SDB) gradually replaced active bathymetry methods such as shipborne echo sounders or airborne LiDAR. Large-scale surveys by satellite image can effectively improve the disadvantages of active telemetry including high cost, time-consuming, and susceptible to climate. Especially for traditional shipborne echo sounders, because of navigational safety, shallow water areas are often not accessible. Most of the bathymetry estimation performed by optical satellites were based on two kinds of methods, physical method and empirical method, based on Beer-Lambert Law, such as ratio algorithm, look-up table (LUT) and experience learning method. However, these methods do not consider the mixture of the bottom material on the sea bottom, but treat each pixel as a pure pixel containing only a single endmember.
Since the spatial resolutions of satellite images are usually several meters, most pixels contain more than one materials and can be considered as mixed pixels. This study combines the technology of spectral unmixing and Beer-Lambert Law to explore the relationship of radiance spectra and water depth from geometry point of view. The linear mixture model has been widely used for remote sensing and it assumes that the spectrum of each pixel is a linear combination of pure endmembers, so it is located inside a convex polyhedron with vertices of endmembers in multi-dimension. Based on Beer-Lambert Law, spectral extinction is exponentially related to the water depth, and the extinction coefficient of the spectral bands are different and independent, the polyhedron will rotate and shrink with the increasing of water depth. This research proposes a new bathymetry estimation algorithm by combining spectral unmixing and Beer-Lambert law to not only improve the accuracy of coastal bathymetric mapping, but also estimate the spectral signatures of endmembers and their abundances on the seabed. In addition, the spectral unmixing technology enables this algorithm to estimate the depth of water in satellite images with lower spatial resolution. This study observes the feasibility of this theory under different conditions through four simulations. The experimental results showed that this algorithm requires enough pixels with purity level greater than 0.9 and evenly distributed close to water surface to reach good estimation in water depth.

關鍵字(中)

★ 水深估計
★ 光譜解混
★ 多光譜衛星影像

關鍵字(英)

★ Bathymetry Estimation
★ Spectral Unmixing
★ Satellite Derived Bathymetry

論文目次

摘要 i
Abstract iii
誌謝 v
Content vi
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1-1 General Background Information 1
1-2 Motivation and Objective 5
1-3 The Thesis Organization 6
Chapter 2 Literature Review 7
2-1 Bathymetry Estimation 7
2-2 Hyperspectral Unmixing 10
Chapter 3 Methodology 14
3-1 Research Design 14
3-2 General Principle 15
3-2-1 Beer-Lambert Law 15
3-2-2 Multispectral Unmixing 16
3-2-3 Ratio Algorithms 16
3-2-4 Log-transform 18
3-3 Multispectral Unmixing 19
3-3-1 Linear Mixing Model (LMM) 20
3-3-2 Convex Geometry 21
3-3-3 Hyperplane-based Craig-simplex-identification (HyperCSI) 22
3-4 Hyperplane in Zero Water Depth 26
3-4-1 Principal Component Analysis (PCA) 26
3-4-2 Linear Regression 28
3-4-3 Regression Pixels of Hyperplane 29
Chapter 4 Experimental Results 35
4-1 Performance Measure 35
4-2 Simulated Data 36
4-2-1 Diversity of Total Pixel Number and Abundance 43
4-2-2 Diversity of Endmembers Distributed in Shallow Water 52
4-2-3 Diversity of Endmembers Distributed in Deep Water 60
4-2-4 Estimate Bathymetry in Low Spectral Resolution 63
4-3 Experiment 69
4-4 Discussion 71
Chapter 5 Conclusions 73
5-1 Summary 73
5-2 Future works 74
References 76

參考文獻

[1] S. Lee, "Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data," International Journal of Remote Sensing, vol. 26, no. 7, pp. 1477-1491, 2005.
[2] G. Metternicht, L. Hurni, and R. Gogu, "Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments," Remote sensing of Environment, vol. 98, no. 2-3, pp. 284-303, 2005.
[3] J. Sanyal and X. Lu, "Application of remote sensing in flood management with special reference to monsoon Asia: a review," Natural Hazards, vol. 33, no. 2, pp. 283-301, 2004.
[4] D. M. Tralli, R. G. Blom, V. Zlotnicki, A. Donnellan, and D. L. Evans, "Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 59, no. 4, pp. 185-198, 2005.
[5] L. Dong and J. Shan, "A comprehensive review of earthquake-induced building damage detection with remote sensing techniques," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 84, pp. 85-99, 2013.
[6] S. Stramondo, C. Bignami, M. Chini, N. Pierdicca, and A. Tertulliani, "Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies," International Journal of Remote Sensing, vol. 27, no. 20, pp. 4433-4447, 2006.
[7] M. A. Lefsky, W. B. Cohen, G. G. Parker, and D. J. Harding, "Lidar Remote Sensing for Ecosystem Studies: Lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists," BioScience, vol. 52, no. 1, pp. 19-30, 2002.
[8] D. A. Stow et al., "Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems," Remote sensing of environment, vol. 89, no. 3, pp. 281-308, 2004.
[9] M. K. Ridd, "Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities," International journal of remote sensing, vol. 16, no. 12, pp. 2165-2185, 1995.
[10] J. Xiao et al., "Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing," Landscape and urban planning, vol. 75, no. 1-2, pp. 69-80, 2006.
[11] W. W. Immerzeel, P. Droogers, S. De Jong, and M. Bierkens, "Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing," Remote sensing of Environment, vol. 113, no. 1, pp. 40-49, 2009.
[12] J. Yang et al., "The role of satellite remote sensing in climate change studies," Nature climate change, vol. 3, no. 10, pp. 875-883, 2013.
[13] P. Gupta, S. A. Christopher, J. Wang, R. Gehrig, Y. Lee, and N. Kumar, "Satellite remote sensing of particulate matter and air quality assessment over global cities," Atmospheric Environment, vol. 40, no. 30, pp. 5880-5892, 2006.
[14] R. V. Martin, "Satellite remote sensing of surface air quality," Atmospheric environment, vol. 42, no. 34, pp. 7823-7843, 2008.
[15] 薛憲文, 史天元, and 徐佳筠, "水域測深方法暨原理探討," 航測及遙測學刊, vol. 16, no. 3, pp. 203-217, 2012.
[16] J. F. Bramante, D. K. Raju, and T. M. Sin, "Multispectral derivation of bathymetry in Singapore′s shallow, turbid waters," International journal of remote sensing, vol. 34, no. 6, pp. 2070-2088, 2013.
[17] J. S. Bhatt and M. V. Joshi, Regularization in Hyperspectral Unmixing. SPIE Spotlights, 2016.
[18] D. R. Lyzenga, "Passive remote sensing techniques for mapping water depth and bottom features," Applied optics, vol. 17, no. 3, pp. 379-383, 1978.
[19] D. R. Lyzenga, "Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data," International Journal of Remote Sensing, vol. 2, no. 1, pp. 71-82, 1981/01/01 1981.
[20] Z. Lee, K. L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch, "Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization," Applied optics, vol. 38, no. 18, pp. 3831-3843, 1999.
[21] E. M. Louchard, R. P. Reid, F. C. Stephens, C. O. Davis, R. A. Leathers, and D. T. Valerie, "Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach," Limnology and Oceanography, vol. 48, no. 1part2, pp. 511-521, 2003.
[22] J. Kibele and N. T. Shears, "Nonparametric empirical depth regression for bathymetric mapping in coastal waters," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 11, pp. 5130-5138, 2016.
[23] F. C. Polcyn, W. L. Brown, and I. J. Sattinger, "The measurement of water depth by remote sensing techniques," in "Willow Run Laboratories," MICHIGAN UNIV ANN ARBOR INST OF SCIENCE AND TECHNOLOGY1970, vol. Report 8973-26-F.
[24] C. Wezernak and D. Lyzenga, "Analysis ofCladophora distribution in Lake Ontario using remote sensing," Remote Sensing of Environment, vol. 4, pp. 37-48, 1975.
[25] W. D. Philpot, "Bathymetric mapping with passive multispectral imagery," Applied optics, vol. 28, no. 8, pp. 1569-1578, 1989.
[26] R. P. Stumpf, K. Holderied, and M. Sinclair, "Determination of water depth with high‐resolution satellite imagery over variable bottom types," Limnology and Oceanography, vol. 48, no. 1part2, pp. 547-556, 2003.
[27] Z. Lee, K. L. Carder, R. F. Chen, and T. G. Peacock, "Properties of the water column and bottom derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data," Journal of Geophysical Research: Oceans, vol. 106, no. C6, pp. 11639-11651, 2001.
[28] C. Giardino, V. E. Brando, A. G. Dekker, N. Strömbeck, and G. Candiani, "Assessment of water quality in Lake Garda (Italy) using Hyperion," Remote Sensing of Environment, vol. 109, no. 2, pp. 183-195, 2007.
[29] J. A. Goodman and S. L. Ustin, "Classification of benthic composition in a coral reef environment using spectral unmixing," Journal of Applied Remote Sensing, vol. 1, no. 1, p. 011501, 2007.
[30] W. M. Klonowski, P. R. Fearns, and M. J. Lynch, "Retrieving key benthic cover types and bathymetry from hyperspectral imagery," Journal of Applied Remote Sensing, vol. 1, no. 1, p. 011505, 2007.
[31] N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE signal processing magazine, vol. 19, no. 1, pp. 44-57, 2002.
[32] J. M. Bioucas-Dias et al., "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE journal of selected topics in applied earth observations and remote sensing, vol. 5, no. 2, pp. 354-379, 2012.
[33] W.-K. Ma et al., "A signal processing perspective on hyperspectral unmixing: Insights from remote sensing," IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 67-81, 2013.
[34] D. C. Heinz, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery," IEEE transactions on geoscience and remote sensing, vol. 39, no. 3, pp. 529-545, 2001.
[35] J. W. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," 1993.
[36] M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Imaging Spectrometry V, 1999, vol. 3753, pp. 266-275: International Society for Optics and Photonics.
[37] C.-C. Wu, S. Chu, and C.-I. Chang, "Sequential n-findr algorithms," in Imaging Spectrometry XIII, 2008, vol. 7086, p. 70860C: International Society for Optics and Photonics.
[38] J. M. Nascimento and J. M. Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, 2005.
[39] C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE transactions on geoscience and remote sensing, vol. 44, no. 10, pp. 2804-2819, 2006.
[40] T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, "A simplex volume maximization framework for hyperspectral endmember extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4177-4193, 2011.
[41] M. Craig, "Unsupervised unmixing of remotely sensed images," in Proceedings Fifth Australasian Remote Sensing Conference, Perth Western Australia, 1990, vol. 8, no. 12, p. 1990.
[42] M. D. Craig, "Minimum-volume transforms for remotely sensed data," IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 3, pp. 542-552, 1994.
[43] L. Miao and H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 3, pp. 765-777, 2007.
[44] J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, "Hyperspectral remote sensing data analysis and future challenges," IEEE Geoscience and remote sensing magazine, vol. 1, no. 2, pp. 6-36, 2013.
[45] D. Heinz, C.-I. Chang, and M. L. Althouse, "Fully constrained least-squares based linear unmixing [hyperspectral image classification]," in IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS′99 (Cat. No. 99CH36293), 1999, vol. 2, pp. 1401-1403: IEEE.
[46] C.-H. Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, "A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing," IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 1946-1961, 2015.
[47] S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and intelligent laboratory systems, vol. 2, no. 1-3, pp. 37-52, 1987.
[48] H. Abdi and L. J. Williams, "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459, 2010.
[49] H. B. Demuth, M. H. Beale, O. De Jess, and M. T. Hagan, Neural network design. Martin Hagan, 2014.
[50] S. H. Brown, "Multiple linear regression analysis: a matrix approach with MATLAB," Alabama Journal of Mathematics, vol. 34, pp. 1-3, 2009.
[51] T.-H. Chan, C.-Y. Chi, Y.-M. Huang, and W.-K. Ma, "A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing," IEEE Transactions on Signal Processing, vol. 57, no. 11, pp. 4418-4432, 2009.
[52] WorldView-2 Satellite Sensor. (2009). [Internet]. Available: https://www.satimagingcorp.com/satellite-sensors/worldview-2/
[53] C. D. Mobley, Light and water: radiative transfer in natural waters. Academic press, 1994.

指導教授

任玄(Ren, Hsuan)

審核日期

2020-12-2

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