第五章 結論與建議
第二節 建議
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立 政 治 大 學
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第二節 建議
五項建議，分別為：
（一）率定近紅外光影像
本研究解算近紅外光影像時，所使用的內方位參數為率定可見光 影像所得之資料，而根據光線折射原理，近紅外光波段，折射率勢必 與可見光者不同，因此於解算近紅外光影像，若以其率定所得之內方 位參數平差解算，並產製近紅外光點雲資料，應可再提升精度。
（二）採用自率光束法平差一併求解內方位參數
實驗使用的近景攝影測量軟體 Image Master，雖然提供光束法平 差解算攝影三角測量，但未支援自率光束法平差（self calibration bundle adjustment），因此僅能以事先率定的相機內方位參數，平差解算物空 間坐標，若改採提供自率光束法平差的攝影測量軟體，於平差時一併 求解各影像之內方位參數，應可提升內方位參數之精度。
（三）改變 NCC 與 LSM 之模版尺寸和灰階變異門檻值
本研究使用 Image Master 中提供的 NCC 及 LSM 兩種影像匹配法 自動產製點雲資料時，影像匹配的模版尺寸和灰階變異門檻值均為軟 體之預設值，後續研究如使用 Image Master 產製點雲資料，可考慮變 更模版尺寸與灰階變異門檻值兩項參數。
（四）嘗試使用不同影像匹配演算法
Image Master 提供最常使用的區域匹配法 NCC 與 LSM，但尚有其
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影像匹配法不同於 Image Master 的軟體，以比較其異同。
（五）萃取近紅外光點雲之光譜資訊，用於輔助點雲分類。
加入近紅外光波段匹配影像，產製點雲資料和僅使用可見光影像 匹配產製點雲的最大區別，為增加近紅外光影像所包含的豐富光譜資 訊，若能有效利用此資訊，產製包含幾何與光譜資訊兼具的點雲，將 可有效利用此光譜資訊，輔助點雲分類，濾除 DSM 上之附加物，進而 建立其 DEM。
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scanning”, ISPRS Journal of Photogrammetry & Remote Sensing, 54: 83-94.Charaniya, A. P., R. Manduchi, and S. K. Lodha, 2004, ”Supervised Parametric Classification of Aerial LiDAR Data”, paper presented at the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Washington, DC, US, June 27-July 2.
Detchev, I., A. Habib, and J. Y. Rau, 2011, “Image Matching for 3D Photogrammetric Reconstruction”, paper presented at the 32nd, Asian Conference on Remote Sensing, Taipei, Taiwan, October 3-7.
Fritsch, D., A. M. Khosravani, A. Cefalu, and K. Wenzel, 2011, “Multi-Sensors and Multiray Reconstruction for Digital Preservation”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.
Henricsson, O., F. Bignone, W. Willuhn, F. Ade, O. Kübler, E. Baltsavias, S.
Mason, and A. Grün, 1996, ‚“Project AMOBE: Strategies, Current Status and Future Work”, International Archives of Photogrammetry and Remote Sensing, paper presented at the 18th ISPRS Congress, Vienna, Austria, July 9-19.
Haala, N., and C. Brenner, 1999, “Extraction of buildings and trees in urban environments”, ISPRS Journal of Photogrammetry & Remote Sensing, 54:
130-137.
Hoegner, L., and U. Stilla, 2008, “Case Study of the 5-point Algorithm for Texturing Existing Building Models from Infrared Image Sequences”, International Archives of Photogrammetry, Remote Sensing and Spatial Geoinformation Sciences, 37(B3B): 479-484.
Hoegner, L., and U. Stilla, 2009, “Thermal leakage detection on building facades using infrared textures generated by mobile mapping”, paper presented at Joint Urban Remote Sensing Event 2009, Shanghai, China,
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Jensen, J. R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective, 2nd Edition, US: Pearson Education, Inc.
Kremer, J., 2011, “Power Line Mapping: Data Acquisition with A Specialized Multi-Sensor Platform”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.
Mikhail, E. M., J. S. Bethel, and J. C. McGlone, 2001, Introduction to Modern Photogrammetry, New York: John Wiley & Sons, Inc.
McGlone, J. C., E. M. Mikhail, J. S. Bethel, and R. Mullen, 2004, Manual of Photogrammetry, 5th Edition, Maryland: American Society for Photogrammetry and Remote Sensing.
Minten, H., 2011, “News from IGI”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.
Seager, S., E. L. Turner, J. Schafer, and E. B. Ford, 2005, “Vegetation’s Red Edge: A Possible Spectroscopic Biosignature of Extraterrestrial Plants”, Astrobiology, 5(3): 372-390.
TOPCON Positioning Systems Inc., 2008, “Operation Manual”, Tokyo, Japan.
TOPCON Positioning Systems Inc.
Vosselman, G., and H. G. Maas, 2010, Airborne and Terrestrial Laser Scanning, Scotland, UK: Whittles Publishing.
Wolf, P. R., and B. A. Dewitt, 2004, Elements of Photogrammetry With Applications in GIS, 3rd Edition, US: McGraw-Hill.
三、網頁參考文獻
iWitness (2009). Frequently Asked Questions. Retrieved May 24, 2012 from iWitness on the World Wide Web:
http://www.iwitnessphoto.com/iwitness/faqs.html
Photometrix (2010, August). Users Manual for iWitness and iWitnessPRO.
Retrieved January 9, 2012 from World Wide Web:
http://www.photometrix.com.au
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立 政 治 大 學
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Na tiona
l Ch engchi University
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立 政 治 大 學
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l Ch engchi University
附錄
以下所述內容為採用 TWD97 TM2°分帶坐標系統下，實驗區控制測量 和實地測量所得之控制點及檢核點坐標，其中 E、N 為縱橫線坐標，而 H 為正高。
以 GPS-RTK 求得實驗區左岸道路 8 個已知點坐標，其平面中誤差均在
±0.02 m 以內；而直接水準測量求得之高程中誤差則為 0.001 m。
實驗區對岸道路已知點坐標表 （共 8 個）
點號 E（m） N（m） H（m）
P1 307749.062 2764204.935 16.474 P2 307753.105 2764215.093 16.468 P3 307760.587 2764236.971 16.503 P4 307764.621 2764244.231 16.530 P5 307773.463 2764268.532 16.577 P6 307777.629 2764273.898 16.626 P7 307781.046 2764284.876 16.556 P8 307785.830 2764297.709 16.626
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A 307680.914 2764195.240 19.695 C 307651.890 2764190.099 38.632 D 307677.794 2764203.349 22.491 E 307653.262 2764206.254 38.133 H 307654.368 2764219.635 38.148 J 307683.286 2764220.441 20.606 K 307678.999 2764220.502 22.508 L 307655.566 2764229.800 35.279 R 307656.220 2764253.669 48.314 V 307570.447 2764320.234 76.791 AA 307690.243 2764242.169 18.579 BB 307705.528 2764234.904 14.271 CC 307700.743 2764248.583 16.968 DD 307685.851 2764263.734 22.665 II 307708.441 2764258.573 14.814 JJ 307704.334 2764270.541 18.647 MM 307666.510 2764286.781 46.649 OO 307651.312 2764327.206 51.226 PP 307689.891 2764291.851 22.514 QQ 307670.346 2764302.597 34.879 WW 307691.368 2764302.292 22.649 AAA 307723.450 2764295.958 15.125 CCC 307724.776 2764299.043 14.835 FFF 307676.877 2764357.441 60.253 III 307679.844 2764377.381 60.248 NNN 307731.052 2764327.466 17.166 QQQ 307728.565 2764333.736 19.512 SSS 307728.575 2764333.743 18.724
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B 307651.262 2764187.539 38.634 F 307653.889 2764208.829 38.130 G 307653.796 2764217.275 38.151 I 307682.430 2764211.538 20.395 M 307680.890 2764223.884 22.776 N 307681.083 2764225.120 22.797 O 307681.006 2764223.915 22.204 P 307681.178 2764225.162 22.122 Q 307684.601 2764228.144 20.513 S 307656.640 2764240.311 49.236 T 307657.026 2764256.985 48.326 U 307569.043 2764314.435 76.791 W 307690.147 2764242.043 21.526 X 307690.248 2764242.325 21.524 Y 307690.151 2764242.036 20.745 Z 307690.248 2764242.319 20.745 EE 307686.967 2764269.603 22.862 FF 307687.116 2764270.876 22.874 GG 307687.017 2764269.592 22.263 HH 307687.127 2764270.880 22.266 KK 307665.113 2764276.477 39.315 LL 307666.068 2764283.826 46.641 NN 307667.588 2764294.199 39.333 RR 307651.955 2764335.839 51.219 SS 307671.156 2764317.692 37.115 TT 307673.056 2764325.264 40.016 UU 307673.666 2764328.021 40.022 VV 307675.007 2764335.823 36.687 YY 307719.538 2764297.584 16.172 ZZ 307720.051 2764299.884 16.165 BBB 307724.211 2764297.918 15.125 EEE 307674.638 2764350.195 53.551 GGG 307677.481 2764361.514 60.278 KKK 307679.233 2764373.302 60.263
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RRR 307728.469 2764333.462 18.722
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通訊：[email protected] 學歷： Enhancement by Matching Multispectral Images”, paper presented at the 32nd, Asian Conference on Remote Sensing, Taipei, Taiwan, October 3-7.Chen-Ting Liao, Hao-Hsiung Huang, 2012, “Classification by Using Multispectral Point Cloud Data”, paper presented at the XXII congress of the International Society for Photogrammetry and Remote