多頻譜衛星影像融合與紅外線影像合成

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姓名

陳奕霖(Yi-Ling Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

多頻譜衛星影像融合與紅外線影像合成
(Multi-spectral Image Fusion and Infrared Image Synthesis)

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摘要(中)

在本論文的研究中，我們提出一個以多頻譜遙測影像來合成紅外線影像景觀的方法。這個方法共分成四個步驟：影像融合、地形效應校正、影像複合像點分類、及紅外線頻譜合成。在第一步驟中，我們以淩波轉換 (wavelet transform) 為基礎的主成分分析法 (principal component analysis)來融合高解析度單一頻譜影像及低解析度多頻譜影像成為高解析度的多頻譜影像。在第二步驟中，我們藉由迴歸交點法 (regression intersection method) 與方向餘弦超球體轉換法 (hyperspherical direction cosine transformation) 來消除融合後衛星影像中的地形效應與感應器的偏差。在第三步驟中，我們以線性複合模式 (linear mixing model) 為基礎，利用線性頻譜分析技術 (linear spectral unmixing) 來分解影像中的複合像點成份，並產生各材質的分量影像 (fraction images)。在第四步驟中，我們以 ASTER 頻譜資料庫所提供的材質反應資料結合之前所獲得的材質分量影像，利用線性複合模式合成出特定波長的紅外線影像。在本研究中，我們發現以淩波轉換為基礎的主成份分析法比傳統融合方法在融合時對多頻譜影像能保存更多的頻譜資訊；另外融合後的多頻譜影像在做地形效應修正時，能獲得較好的效果，因而能合成出更正確紅外線影像。最後，我們把此特定波長的紅外線影像貼圖到對應的地形模型 (terrain model) 上以產生 3-D 紅外線影像景觀。

摘要(英)

In this paper, we propose a framework for synthesizing specific infrared images from multi-spectral remote-sensing images. The framework is divided into four stages: image fusion, topographic normalization, mixed-pixel analysis, and spectrum synthesis. Firstly, an image fusion method, wavelet-based principal component analysis, is proposed to fuse low-resolution multi-spectral images (Landsat TM) and high-resolution single band image (SPOT PAN) to generate high-resolution multi-spectral images. Secondly, a regression intersection method (RIM) and a hyperspheric direction cosine (HSDC) transformation are adopted to eliminate atmospheric effects, sensor bias, and topographic effects in generated high-resolution multi-spectral images. Thirdly, a linear spectral unmixing (LSU) method is employed for the mixed-pixel classification on the refined images to produce the material fraction images. Lastly, we incorporate the fraction images with reflectance tables of ASTER spectral library to synthesize specific infrared images. The proposed image fusion method really produces the high-resolution multi-spectral images preserving most original spectral information. The fusion method also improves the effect of topographic normalization and hence enhances the quality of the final synthesis images. The synthesis images will be mapped onto the corresponding terrain models to generate realistic infrared images for flight and tactical simulation.

關鍵字(中)

★ 紅外線影像合成
★ 影像融合
★ 多頻譜衛星影像

關鍵字(英)

★ Infrared Image Synthesize
★ Image Fusion
★ Muti-spectral Image

論文目次

Abstractii
Contentsiii
List of Figuresv
List of Tablesvii
Chapter 1 Introduction1
1.1 Motivation1
1.2 System overview2
1.2.1 Wavelet-based image fusion3
1.2.2 Topographic normalization4
1.2.3 Mixed pixel analysis4
1.2.4 Synthesizing high-resolution infrared images5
1.3 Thesis organization5
Chapter 2 Related Works7
2.1 Image fusion approaches7
2.2 Correct topographic and atmospheric effects9
2.2.1 Correct topographic and atmospheric effect with ancillary data10
2.2.2 Correct topographic and atmospheric effect without ancillary data11
2.3 Spectral mixture analysis12
2.3.1 Geometric mixing models12
2.3.2 Linear mixing models13
2.3.3 Nonlinear mixing models15
Chapter 3 Image Fusion17
3.1 Wavelet transform17
3.2 Wavelet-based image fusion20
3.3 Wavelet-based PCA image fusion22
Chapter 4 Topographic Normalization27
4.1 Introduction of the topographic and atmospheric effects27
4.2 The RIM and HSDC method30
4.2.1 Using the RIM method to eliminate atmospheric effect and sensor bias32
4.2.2 Using the HSDC method to eliminate topographic effect35
Chapter 5 Mixed-Pixel Analysis and Spectral Reflectance Synthesis38
5.1 The Linear spectral unmixing (LSU) method39
5.2 Spectral reflectance synthesis43
Chapter 6 Experiments and Discussion45
6.1 Experiments45
6.1.1 Image fusion45
6.1.2 Topographic normalization50
6.1.3 Synthesize the specific infrared image54
6.2 Discussions56
Chapter 7 Conclusions58
References60

參考文獻

[1]Adams, J. B., D. E. Sabol, V. Kapos, R. A. Filho, D. A. Roberts, M. O. Smith, and R. Gillespie, "Classification of multispectral images based on fraction of endmembers: application to land-cover change in the Brazilian Amazon," Remote Sens. Environ., Vol.52, pp.137-154, 1995.
[2]Asner, G. P., C. A. Wessman, and J. L. Privette, "Umixing the directional reflectances of AVHRR sub-pixel landcovers," IEEE Trans. Geosci. Remote Sensing, Vol.35, No.4, pp.868-878, 1997.
[3]Borel, C. C. and S. A. W. Gerstl, "Nonlinear spectral mixing models for vegetative and soil surfaces," Remote Sens. Environ., Vol.47, pp.403-416, 1994.
[4]Bosdogianni, P., M. Petrou, and J. Kittler, "Mixture models with higher order moments," IEEE Trans. Geosci. Remote Sensing, Vol.35, No.2, pp.341-353, 1997.
[5]Boudereau, E., R. Huguenin, and M. Karaska, "Nonparametric classification of subpixel materials in multispectral imagery," in Proc. SPIE, Vol.2758, 1996, pp.31-39.
[6]Burt, P. J. and R. J. Kolczynski, "Enhanced image capture through fusion," in Proc. 4th Int. Conf. on Computer Vision, 1993, pp.173-182.
[7]Carper, W. J., T. M. Lillesand, and R. W. Kiefer, "The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multi-spectral image data," Photogrammetric Engineering & Remote Sensing, Vol.56, No.4, pp.459-467, 1990.
[8]Chavez, P. S. Jr., "Digital merging of Landsat TM and digitized NHAP data for 1:24,000-scale image mapping," Photogrammetric Engineering & Remote Sensing, Vol.52, No.10, pp.1637-1646, 1986.
[9]Chavez, P. S. Jr. and A. Y. Kwarteng, "Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis," Photogrammetric Engineering & Remote Sensing, Vol.55, No.3, pp.339-348, 1989.
[10]Chavez, P. S. Jr., and J. A. Bowell, "Comparison of the spectral information content of Landsat thematic mapper and SPOT for three different sites in the phoenix, Arizona region," Photogrammetric Engineering & Remote Sensing, Vol.54, No.12, pp.1699-1708, 1988.
[11]Chavez, P. S. Jr., S. C. sides, and J. A. Anderson, "Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic," Photogrammetric Engineering & Remote Sensing, Vol.57, No.3, pp.295-303, 1990.
[12]Chen, W.-F. and H.-H. Cheng, "Applying linear regression model in topographic effect correction of SPOT image," Journal of Soil and Water Conservation, Vol.28, No.4, pp.101-115, 1996.
[13]Chen, X., "Topographic normalization of TM imagery for rock classification in Pershing county, Nevada," http://lapis.geo.wvu.edu/geog455/spring98/12/intro.htm
[14]Chipman, L. J. and L. N. Graham, "Wavelets and image fusion," in SPIE, Vol. 2569, 1995, pp.208-219.
[15]Civco, D. L., "Topographic normalization of Landsat thematic mapper digital imagery," Photogrammetric Engineering & Remote Sensing, Vol.55, No.9, pp.1303-1309, 1989.
[16]Colby, J. D., "Topographic normalization in rugged terrain," Photogrammetric Engineering & Remote Sensing, Vol.57, No.5, pp.531-537, 1991.
[17]Conese, C., M. A. Gilabert, F. Maselli, and L. Bottai, "Topographic normalization of TM scense throught the use of an atmosperic correction method and digital terrain models," Photogrammetric Engineering & Remote Sencing, Vol.59, No.12, pp.1745-1753, 1993.
[18]Crippen, R. E., "The regression intersection method of adjusting image data for band ratioing," INT. J. Remote Sensing, Vol.8, No.2, pp.137-155, 1987.
[19]Ekstrand, S., "Landsat TM-based forest damage assessment: correction for topographic effects," American society for Photogrammetry and Remote Sensing, Vol.62, No.2, pp.151-161, 1996.
[20]Garguet-Duport, B., J. Girel, J.-M. Chassery, and G. Pautou, "The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data," Photogrammetric Engineering & Remote Sensing, Vol.62, No.9, pp.1057-1066, 1996.
[21]Gong, P., J. R. Miller, and M. Spanner, "Forest canopy closure from classification and spectral unmixing of scene components - multisensor evaluation of an open canopy," IEEE Trans. Geosci. Remote Sensing, Vol.32, No.5, pp.1067-1080, 1994.
[22]Harsanyi, J. C. and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sensing, Vol.32, No.4, pp.779-785, 1994.
[23]Holben, B. N. and Y. E. Shimabukuro, "Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors," Int. J. Remote Sensing, Vol.14, No.11, pp.2231-2240, 1993.
[24]Hsin, H.-C. and C.-C. Li, "Wavelet-based filtering in scale space for data fusion," in SPIE, Vol.2569, pp.701-712, 1995.
[25]Hu, Y. H., H. B. Lee, and F. L. Scarpace, "Optimal lineat spectral unmixing," IEEE Trans. Geosci. Remote Sensing, Vol.37, No.1, pp.639-644, 1999.
[26]Li, X. and A. H. Strahler, "Geometric-optical modeling of a conifer forest canopy," IEEE Trans. Geosci. Remote Sensing, Vol.23, No.5, pp.705-721, 1985.
[27]Li, H., B. S. Manjunath, and S. K. Mitra, "Multisensor image fusion using the wavelet transform," Graphical Models and Image Processing, Vol.57, No.3, pp.235-245, 1995.
[28]Luo, R. and M. Kay, "Data fusion and sensor integration: state of art in 1990s," in Data Fusion in Robotics and Machine Intelligence, M. Abidi and R. Gonzalez, eds., Academic Press, San Diego, 1992, pp.7-136.
[29]Nunez, J., X. Otazu, O. Fors, A. Prades, V. Pala, and R. Arbiol, "Multiresolution-based image fusion with additive wavelet decomposition," IEEE Trans. on Geoscience and Remote Sensing, Vol.37, No.3, pp.1204-1211, 1999.
[30]Pellemans, A. H. J. M., R. W. L. Jordans, and R. Allewijn, "Merging multispectral and panchromatic SPOT images with respect to the radiometric properties of the sensor," Photogrammetric Engineering & Remote Sensing, Vol.59, No.1, pp.81-87, 1993.
[31]Pouch, G. W. and D. J. Campagna, "Hyperspherical direction cosine transformation for separation of spectral and illumination information in digital scanner data," Photogrammetric Engineering & Eemote Sensing, Vol.56, No.4, pp.475-479, 1990.
[32]Ray, T. W. and B. C. Murray, "Nonlnear spectral mixing in desert vegetation," Remote Sens. Environ., Vol.55, No.1, pp.59-64, 1996.
[33]Rowan, L. C., Wetlaufer, P. H., Geotz, A. F. H., Billingsley, F. C., and Stewart, J. H., "Discrimination of hydrothermally altered areas and of rock types using computer enhanced ERTS images," South Central Nevada., U.S. Geological Survey Professional, 1974, pp.883.
[34]Schaaf, C. B., X. Li, and A. H. Strahler, "Topographic effects on bi-directional and hemispherical reflectances calculated with a geometric-optical canopy model," IEEE Trans. on Geoscience and Remote Sensing, Vol. 32, No. 6, pp.1186-1193, 1994.
[35]Settle, J. J., "On the relationship between spectral unmixing and subspace projection," IEEE Trans. Geosci. Remote Sensing, Vol.34, No.4, pp.1045-1046, 1996.
[36]Shimabukuro, Y. K. and J. A. Smith, "The least-squares mixing models to generate fraction images derived from remote sensing multispectral data," IEEE Trans. Geosci. Remote Sensing, Vol.29, No.1, pp.16-20, 1991.
[37]Smith J., T. Lin, and K. Ranson, "The Lambertian assumption and Landsat data," Photogrammetric Engineering & Remote Sensing, Vol.46, No.9, pp.1183-1189, 1980.
[38]Toet, A. "Multiscale contrast enhancement with application to image fusion," Optical Engineering, Vol.31, No.5, pp.1026-1031, 1992.
[39]Tu, T.-M., C.-H. Chen, and C.-I. Chang, "A posteriori least squares orthogonal subspace projection approach to desired signature extraction and detection," IEEE Trans. Geosci. Remote Sensing, Vol.35, No.1, pp.127-139, 1997.
[40]Tu, T.-M., C.-H. Chen, and C.-I. Chang, "A noise subspace projection approach to target signature detection and extraction in an unknown background for hyperspectral images," IEEE Trans. Geosci. Remote Sensing, Vol.36, No.1, pp.171-181, 1998.
[41]Tu, T.-M., H.-C. Shyu, C.-H. Lee, and C.-I. Chang, "An oblique subspace projection approach to mixed pixel classification in hyperspectral images," submitted for publication.
[42]Wald, L., T. Ranchin, and M. Mangolini, "Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images," Photogrammetric Engineering & Remote Sensing, Vol.63, No.6, pp.691-699, 1997.
[43]Yocky, D. A., "Multiresolution wavelet decomposition image merger of Landsat thematic mapper and SPOT panchromatic data," Photogrammetric Engineering & Remote Sensing, Vol.62, No.9, pp.1067-1074, 1996.

指導教授

曾定章(Din-chang Tseng)

審核日期

2000-7-6

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