應用多光譜遙測影像進行線性及非線性 水深反演模式之探討;Log-linear and Nonlinear Inversion Model for Bathymetry in Multispectral Remote Sensing Images

NCUIR > Research Center > Master of Science Program in Remote Sensing Science and Technology > Electronic Thesis & Dissertation > Item 987654321/65870

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65870

Title:	應用多光譜遙測影像進行線性及非線性水深反演模式之探討;Log-linear and Nonlinear Inversion Model for Bathymetry in Multispectral Remote Sensing Images
Authors:	王元才;Yuan-Tsai,Wang
Contributors:	遙測科技碩士學位學程
Keywords:	對數線性;多光譜;測深;Log-linear Inversion Model;log-linear;Multispectral;Bathymetry;Nonlinear Inversion Model
Date:	2014-07-30
Issue Date:	2014-10-15 17:16:13 (UTC+8)
Publisher:	國立中央大學
Abstract:	摘要水深測量一直都是先進國家致力發展的重要技術。尤其對海岸與海洋管理、航運計畫和環境保育都是關鍵的基礎資訊。台灣地處歐亞大陸與太平洋之間是為亞太海運的樞紐航道，同時週邊海域也是全球海洋生態的熱點。水下地形對我們在進行相關環境保育及管理決策時是重要的依據。近年來由於設備及資訊科學的進步，測深技術也得到了快速的進展。在本論文中，我們採用了福衛二號所拍攝到的澎湖本島南方海域影像為素材，並以船測水深為參考，以線性及非線性的演算法針逆推藍光及綠光在水中衰減的數學模式，藉此得到之水深估計及誤差估計結果。在本研究的結果可以看出在淺水環境中，線性的估算結果比非線性的結果要較準確。在深水環境中，非線性的估算結果則比線性有更好的準確性。在比較兩組不同時期衛星影像分別以對數線性及非線性反演之後，對數線性演算結果的穩定性明顯高於非線性的結果。同時我們也可以看得出在水深估算結果上有明顯的海水表面反射效應，在深水區尤其明顯。我們因此提出了以近紅外做為針對兩種演算模式的水深校正法。對於此校正方式，線性估算模式的穩定性明顯優於非線性。本研究亦嘗修正衛星影像之潮位差，結果與未修正之結果差異不大，只有對非線性的結果有數公分之改進。在本研究中，我們可以明顯看出線性及非線性的對水深估算的優劣所在，因此我們也根據其均方根誤差提出結合兩者的可行性方案，以增加其精確度。 ;Abstract Bathymetry is fundamental and vital task to coastal and marine management, navigational planning, and scientific studies of marine environments for Taiwan due to the hinge position. There are many algorithms proposed in bathymetry estimation recently due to the advance development on computing and instrument technology. We adopted the well-known log-linear and nonlinear inversion model to estimate the water depth of the area in southern of Pengho Island in our study. The blue band and green band images are captured by FORMOSAT-2 multispectral sensor and the ground truth is surveyed with shipborne sounding. The result is affected by the surface reflection. The surface reflectance effect is shown for both models, especially in deep water area. Thus, we proposed a spectral correction based on the NIR band. In our estimation, the log-linear model derives accurate results than nonlinear model in shallow water area. On the other hand in deep water area, the nonlinear model performs better. From the RMS of the estimation, the log-linear is more stable than nonlinear for the NIR spectral correction. In this study, it is illustrated as a practical method to combine the results from log-linear and nonlinear inversion model based on the vary RMS on estimated depth.
Appears in Collections:	[Master of Science Program in Remote Sensing Science and Technology ] Electronic Thesis & Dissertation

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