(44.192.112.123) 您好!臺灣時間:2021/03/06 07:20
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:鄒毅兪
研究生(外文):Yi-Yu Zou
論文名稱:不同地形因子對於高光譜影像輻射改正之影響探討
論文名稱(外文):Impact analysis of different terrain factors on radiometric correction for hyperspectral images
指導教授:徐百輝徐百輝引用關係
口試日期:2017-07-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:77
中文關鍵詞:光譜影像地形改正數值地表模型光達點雲
外文關鍵詞:Hyperspectral imageDigital Surface Model (DSM)Lidar Point CloundsTopographic correction
相關次數:
  • 被引用被引用:0
  • 點閱點閱:84
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
高光譜影像含有較細緻且豐富的光譜資訊,不同物質擁有其獨特的光譜反射曲線,由於受到大氣及地形的影響,光譜影像需事先進行輻射校正,才能因應各種實際需求,但大多數研究仍致力於去除大氣效應,並提出改正模型,如常見的大氣改正模型:MODTRAN、FASCODE、FLAASH、QUAC等,而對於地形效應的影響予以簡化甚至忽略,即便有使用地形改正模型,通常會簡化參數,將入射平面視為蘭伯特平面,僅利用一個地形傾斜角進行簡單的地形改正,雖然也有研究嘗試將反射平面視作非蘭伯特平面,但卻鮮少同時考量地形資料的影響。過去有關地形效應輻射改正的研究大都使用DEM資料計算地形起伏變化,隨著影像空間解析度提升,已無法忽略地表的建物、植被,若能採用DSM資料,將更能呈現改正時的真實地貌,此外,在資料層面上無論是DEM或DSM資料皆已經過人為處理,屬於二手資料,並無法保有最原始的地形資訊,故將另外引入一手資料 — 光達點雲。
本研究針對台北市山區之高光譜影像,先透過QUAC方法進行大氣改正,所採用的地形改正方法包括 Cosine Correction、Minnaert、C Normalization、及Modified Minnaert等四種改正模型,另一方面引入點雲資料與不同空間解析度的DSM,最終分析不同地形資料對高光譜影像地形效應改正的影響。
The imaging spectrometers have the ability to acquire the hyperspectral images with rich and subtle spectral information of earth objects. However, the recorded spectral radiance in hyperspectral image is very subject to the variance of circumstance. In order to utilize this data in practical applications, an accurate radiometric correction of the hyperspectral image has to be performed in advance. The correction of radiometry includes two essential parts, one is atmospheric correction and the other one is topographic correction. Atmospheric correction technique removes spectral atmospheric transmission and scattered path radiance; topographic correction technique removes the effect of topography because of rugged surface areas. Many atmospheric correction models such as MODTRAN, FASCODE, FLAASH, and QUAC are maturing and have been applied in many fields. In contrast, there are many topographic correction models to deal with radiometric correction, however, most of them generally simplify parameters by using cosine correction, which regards the surface as an ideal diffusely reflecting plane (i.e. Lambertian plane). In reality, a simplified model is hard to correct the effect of topography accurately. Although there are a lot of non-Lambertian plane models were proposed, they seldom consider the influence of the types of topographic data. In the past, a small number of studies used DEM data as topographic data. With the increase of image space resolution, it can not ignore the surface of the building, vegetation. Therefore, using DSM data will be more appropriate choice to show the real landscape. In addition, both the DEM and DSM data are second-hand data, and can not maintain the primitive terrain information; hence this research introduces the first-hand data - Lidar point clouds.
The objective of this research is to analyze the influence of different type of topographic data such as the different resolution grid DSM and discrete point clouds. Firstly, the Quick Atmospheric Correction are performed on the hyperspectral image, and then the parameters required by topographic correction model are obtained and improve the quality of topographic correction of the hyperspectral images. Accordingly, this study discusses and analyzes what kind of topographic correction model is suitable for the topographic data and correct the topography effect of the real high resolution hyperspectral images.
口試委員審定書 i
致謝 ii
中文摘要 iii
Abstract iv
目錄 vi
圖目錄 ix
表目錄 xii
第一章 緒論 1
1.1研究背景 1
1.2研究動機與目的 6
1.3論文架構 8
第二章 文獻回顧 9
2.1光譜影像 9
2.2光達系統―點雲資料 12
2.3輻射改正 14
2.3.1大氣效應與改正 15
2.3.2地形效應與改正 20
2.4地形資料計算 24
2.4.1數值地表模型(DSM) 24
2.4.2離散光達點雲 26
第三章 研究方法 29
3.1資料預處理 29
3.2大氣改正模型 30
3.3地形改正模型 31
3.3.1蘭伯特方法(Lambertian Method) 32
3.3.2非蘭伯特法(Non-Lambertian Method) 33
3.4地形參數計算 37
3.4.1數值地表模型參數計算 37
3.4.2離散光達點雲參數計算 39
3.5成果評估 42
3.5.1客觀輻射值分析 42
3.5.2主觀影像應用分析 42
第四章 實驗與成果分析 44
4.1實驗流程 44
4.2實驗區域與資料介紹 46
4.3資料處理 47
4.3.1地形資料產製 47
4.3.2坡度、坡向、太陽光照度角圖產製 49
4.4實驗成果分析 51
4.4.1大氣改正 51
4.4.2實驗Ⅰ—不同空間解析度DSM之影響 52
4.4.3實驗Ⅱ—不同地形資料(點雲、DSM)之影響 56
第五章 結論與建議 71
5.1結論 71
5.2建議 72
參考文獻 73
Bernstein, L. S., Adler-Golden, S. M., Sundberg, R. L., Levine, R. Y., Perkins, T. C., Berk, A., Ratkowski, A. J., Felde, G., and Hoke, M. L., 2005. A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi-and hyperspectral imaging sensors: QUAC (QUick Atmospheric Correction), Proceedings of the IGARSS, 25-29 July, Seoul, Korea.
Bernstein, L. S., Jin, X., Gregor, B., and Adler-Golden, S. M., 2012. Quick atmospheric correction code: algorithm description and recent upgrades. Optical engineering, 51(11), 111719-1.
ENVI, A. C. M., 2009. QUAC and FLAASH User’s Guide. Atmospheric Correction Module Version 4.7, Boulder, CO: ITT Visual Information Solutions.
Gao, B. C., Montes, M. J., Davis, C. O., and Goetz, A. F., 2009. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sensing of Environment, 113, S17-S24.
Gao, Y., and Zhang, W., 2009. A simple empirical topographic correction method for ETM+ imagery. International Journal of Remote Sensing, 30(9), 2259-2275.
Gao, M. L., Zhao, W. J., Gong, Z. N., Gong, H. L., Chen, Z., and Tang, X. M., 2014. Topographic correction of ZY-3 satellite images and its effects on estimation of shrub leaf biomass in mountainous areas. Remote Sensing, 6(4), 2745-2764.
Guy, G. and Medioni, G., 1997. Inference of surfaces, 3D curves, and junctions from sparse, noisy, 3D data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(11), 1265-1277.
Jones, K. H., 1998. A comparison of algorithms used to compute hill slope as a property of the DEM. Computers & Geosciences, 24(4), 315-323.
Lillesand, T., Kiefer, R. W., and Chipman, J. , 2007. Remote sensing and image interpretation. John Wiley and Sons, 804 p.
Lynn Jenner, 2007. NASA Software Enables Satellites to Troubleshoot in Space, NASA Official Web, URL:
https://www.nasa.gov/vision/earth/lookingatearth/software_eo1.html (last date accessed: 29 May 2017).
Mazuk, S., and Lynch, D. K., 2001. Comparison of MODTRAN and FASCODE for Selection of a Cirrus Cloud Detection Band Near the 1.38-micrometers Water Absorption Window (No. TR-2001 (8570)-13V). AEROSPACE CORP EL SEGUNDO CA LAB OPERATIONS.
Meyer, P., Itten, K. I., Kellenberger, T., Sandmeier, S., and Sandmeier, R. (1993). Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment. ISPRS Journal of Photogrammetry and Remote Sensing, 48(4), 17-28.
Minnaert, M., 1941. The reciprocity principle in lunar photometry. The astrophysical journal, 93, 403-410.
Naugle, B. I., and Lashlee, J. D., 1992. Alleviating topographic influences on land-cover classifications for mobility and combat modeling. MURRAY STATE UNIV KY MID AMERICA RME SENSING CENTER.
Phinn, S., Roelfsema, C., Dekker, A., Brando, V., and Anstee, J., 2008. Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sensing of Environment, 112(8), 3413-3425.
Poulos, H. M., and Camp, A. E., 2010. Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. Ecology, 91(4), 1140-1151.
Proy C., D. Tanre and P. Y. Deschamps, 1989. Evaluation of Topographic Effects in Remotely Sensed Data. Remote Sensing of Environment, 30(1), 21-32.
Riaño, D., Chuvieco, E., Salas, J., and Aguado, I., 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003). IEEE Transactions on geoscience and remote sensing, 41(5), 1056-1061.
Richards, J. A., 1999. Remote sensing digital image analysis, Berlin et al.: Springer, Canberra, ACT, Australia, 494 p.
Richter, R., 2011. Atmospheric Correction Methods for Optical Remote Sensing Imagery of Land. Advances in Environmental Remote Sensing: Sensors, Algorithms and Applications, 163-171.
Richter, R., Kellenberger, T., and Kaufmann, H., 2009. Comparison of topographic correction methods. Remote Sensing, 1(3), 184-196.
Richter, R., and Schläpfer, D., 2002. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. International Journal of Remote Sensing, 23(13), 2631-2649.
Rothman, L. S., C. P. Rinsland, A. Goldman, S. T. Massie, D. P. Edwards, J.-M. Flaud, A. Perrin, C. Camy-Peyret, V. Dana, J.-Y. Mandin, J. Schroeder, A. McCann, R. R. Gamache, R. B. Wattson, K. Yoshino, K. V. Chance, K. W. Jucks, L. R. Brown,V. Nemtchinov, and P. Varanasi, 1998. The HITRAN molecular spectroscopic database and HAWKS (HITRAN atmospheric workstation): 1996 edition, Journal of Quantitative Spectroscopy and Radiative Transfer, 60(5): 665-710.
Rouse Jr, J., Haas, R. H., Schell, J. A., and Deering, D. W., 1974. Monitoring vegetation systems in the Great Plains with ERTS.
Schott, J. R., 2007. Remote sensing: the image chain approach, Oxford University Press, USA, 666 p.
Schuster, H. F., 2004. Segmentation of lidar data using the tensor voting framework. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B3), 1073-1078.
Shaw, G. A., and Burke, H. H. K., 2003. Spectral imaging for remote sensing. Lincoln Laboratory Journal, 14(1), 3-28.
Shell, J. R., 2004. Bidirectional reflectance: An overview with remote sensing applications & measurement recommendations. Rochester NY.
Shepherd, J. D., and Dymond, J. R., 2003. Correcting satellite imagery for the variance of reflectance and illumination with topography. International Journal of Remote Sensing, 24(17), 3503-3514.
Smith, J. A., Lin, T. L., and Ranson, K. J.,1980. The Lambertian assumption and Landsat data. Photogrammetric Engineering and Remote Sensing, 46(9), 1183-1189.
Stoney, W. E., 2006. ASPRS guide to land imaging satellites. In NOAA Commercial Remote Sensing Symposium: Key Trends and Challenges in the Global Marketplace.
Teillet, P. M., Guindon, B., and Goodenough, D. G., 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8(2), 84-106.
Thomson, A. G. and Johns, C., 1990. Effects of topography on radiance from upland vegetation in North Wales. International Journal of Remote Sensing, 11(5), 829–840.
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and Reynolds, J. M., 2012. ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
Yang, B., Wei, Z., Li, Q., and Li, J., 2013. Semiautomated building facade footprint extraction from mobile LiDAR point clouds. IEEE Geoscience and Remote Sensing Letters, 10(4), 766-770.
Zhou, Q., and Liu, X., 2004. Analysis of errors of derived slope and aspect related to DEM data properties. Computers & Geosciences, 30(4), 369-378.
李天文、劉學軍、陳正江、湯國安、李軍鋒,2004。規則格網 DEM 坡度坡向演算法的比較分析,乾旱區地理,27(3),398-404。
李粉玲、李京忠、張琦翔,2008。DEM提取坡度•坡向演算法的對比研究,安徽農業科學, 36(17), 7355-7357。
徐百輝,2007。大地的辨識密碼─高光譜影像,科學發展,416,13-19。
郝振純、安貴陽、王加虎、李麗、孟瑾、劉文斌,2011。不同地形下 DEM 的坡度坡向演算法比較,水電能源科學,29(6),68-70。
陳吉龍、武偉、劉洪斌.,2008。 DEM 內插演算法對坡度坡向的影響,水土保持研究,,15(6),14-17。
張瑋,2015。 高光譜影像之地形輻射改正,碩士論文,國立台灣大學,台北市,台灣,69頁。
劉學軍、任志峰、王彥芳、晉蓓,2009。 基於 DEM 的任意方向坡度計算方法,地域研究與開發,28(4),139-141。
劉學軍、張平,2008。DEM 坡度,坡向的有效尺度範圍,武漢大學學報:資訊科學版,,33(12),1254-1258。
羅英哲、曾義星,2009。光達點雲資料面特徵重建,航測及遙測學刊,14(3),171-184。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔