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The Generating Algorithm and Case Study for the Spectral Reflectance Images of Ground Features

Received: 14 March 2016     Accepted: 28 March 2016     Published: 16 April 2016
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Abstract

The paper got the data of spectral reflectance of ground features from field surveying by using field spectroradiometer. The spatial distribution information of the ground features was obtained from the land-use map. Based on above mentioned, the generating algorithm of spectral reflectance image of ground features was developed by Modeler module of ERDAS Imaging software. The four bands were selected as example image bands, including the blue band (0.45-0.52ìm), the green band (0.52-0.60ìm), the red band (0.63-0.69ìm) and the infrared band (0.76-0.90ìm). The four band images with real geographical coordinates were generated from the spectral reflectance of ground features. In order to present the following images, the true color and the standard false color images were merged with four individual band images. By using the field spectroradiometer, relatively simple compared with hyperspectral imaging radiometer, the similar spectral reflectance images of ground features could be obtained with the secondary developed generating algorithm on the ERDAS Imaging software platform. Through the analysis of the spectral reflectance images of ground features, we can prove that the generated images are close to the real land scenes. Therefore, this paper provides a new idea and a new method for the first step of simulating remote sensing images with real geographic coordinates. Finally, the authors prefer to explain that further studies should be developed in two aspects. One issue is how to describe the spatial distributing information of ground features more accurately, and the other is how to differentiate the same class ground features with different spectral reflectance. Based on above, further more studies should include the effect of topographic factors on the spectral reflectance of ground features.

Published in American Journal of Remote Sensing (Volume 4, Issue 2)
DOI 10.11648/j.ajrs.20160402.11
Page(s) 9-12
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

The Spectral Reflectance Image of Ground Features, Generating Algorithm, ERDAS Imaging Software, Field Spectro-radiometer, Merged Image

References
[1] Ting He, Ye Cheng and Jing Wang. 2002. “The method and technology of surveying ground feature spectrum in the wild.” Land Science of China, 16(5): 30-36.
[2] Zhaolu, Zhang. 2010. “The improvement of experimental scheme for the ground feature spectral reflective ratio for the field surveying.” Science of Surveying and Mapping, 35(5): 176-177(175).
[3] Guanter, L., Segl K and Kaufmann H. 2009. “Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission.” IEEE Transactions on Geoscience and Remote Sensing, 47(7 Part 2): 2340-2351.
[4] Fang Chen, Zheng Niu and Chuhong Liao.2006. “The Analysis of method and application of simulating technology of remote sensing.” Geo-information Science, 8(3): 114-118.
[5] Ayman Rashad Elshehaby, Lamyaa Gamal El-deen Taha. 2009. “A new expert system module for building detection in urban areas using spectral information and LIDAR data.” Applied Geomatics, 1(4): 97-110.
[6] Qinqin Sun, Jianjun Tan and Yonghang Xu. 2009. “An ERDAS image processing method for retrieving LST and describing urban heat evolution: a case study in the Pearl River Delta Region in South China.” Environmental Earth Sciences, 59(5): 1047-1055.
[7] Anxin Mei, Wanglu Peng and Qiming Qin. 2001. The Outline of Remote Sensing. Beijing: High Education Press.
[8] Ai-hua Wang, P. F. Hsu and Jiu-ju Cai. 2010. “Modeling bidirectional reflection distribution function of microscale random rough surfaces.” Journal of Central South University of Technology, 17(2): 228-234.
[9] Huiping Liu, Qiming Qin and Wanglu Peng. 2001. The Tutorial of remote sensing practice. Beijing: High Education Press.
[10] Guoan Tang, Youshun Zhang and Yongmei Liu. 2004. The process of Remote sensing Digital Image. Beijing: High Education Press.
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  • APA Style

    Zhaolu Zhang, Yunjun Yao, Haitao Cao. (2016). The Generating Algorithm and Case Study for the Spectral Reflectance Images of Ground Features. American Journal of Remote Sensing, 4(2), 9-12. https://doi.org/10.11648/j.ajrs.20160402.11

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    ACS Style

    Zhaolu Zhang; Yunjun Yao; Haitao Cao. The Generating Algorithm and Case Study for the Spectral Reflectance Images of Ground Features. Am. J. Remote Sens. 2016, 4(2), 9-12. doi: 10.11648/j.ajrs.20160402.11

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    AMA Style

    Zhaolu Zhang, Yunjun Yao, Haitao Cao. The Generating Algorithm and Case Study for the Spectral Reflectance Images of Ground Features. Am J Remote Sens. 2016;4(2):9-12. doi: 10.11648/j.ajrs.20160402.11

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  • @article{10.11648/j.ajrs.20160402.11,
      author = {Zhaolu Zhang and Yunjun Yao and Haitao Cao},
      title = {The Generating Algorithm and Case Study for the Spectral Reflectance Images of Ground Features},
      journal = {American Journal of Remote Sensing},
      volume = {4},
      number = {2},
      pages = {9-12},
      doi = {10.11648/j.ajrs.20160402.11},
      url = {https://doi.org/10.11648/j.ajrs.20160402.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20160402.11},
      abstract = {The paper got the data of spectral reflectance of ground features from field surveying by using field spectroradiometer. The spatial distribution information of the ground features was obtained from the land-use map. Based on above mentioned, the generating algorithm of spectral reflectance image of ground features was developed by Modeler module of ERDAS Imaging software. The four bands were selected as example image bands, including the blue band (0.45-0.52ìm), the green band (0.52-0.60ìm), the red band (0.63-0.69ìm) and the infrared band (0.76-0.90ìm). The four band images with real geographical coordinates were generated from the spectral reflectance of ground features. In order to present the following images, the true color and the standard false color images were merged with four individual band images. By using the field spectroradiometer, relatively simple compared with hyperspectral imaging radiometer, the similar spectral reflectance images of ground features could be obtained with the secondary developed generating algorithm on the ERDAS Imaging software platform. Through the analysis of the spectral reflectance images of ground features, we can prove that the generated images are close to the real land scenes. Therefore, this paper provides a new idea and a new method for the first step of simulating remote sensing images with real geographic coordinates. Finally, the authors prefer to explain that further studies should be developed in two aspects. One issue is how to describe the spatial distributing information of ground features more accurately, and the other is how to differentiate the same class ground features with different spectral reflectance. Based on above, further more studies should include the effect of topographic factors on the spectral reflectance of ground features.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - The Generating Algorithm and Case Study for the Spectral Reflectance Images of Ground Features
    AU  - Zhaolu Zhang
    AU  - Yunjun Yao
    AU  - Haitao Cao
    Y1  - 2016/04/16
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ajrs.20160402.11
    DO  - 10.11648/j.ajrs.20160402.11
    T2  - American Journal of Remote Sensing
    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
    SP  - 9
    EP  - 12
    PB  - Science Publishing Group
    SN  - 2328-580X
    UR  - https://doi.org/10.11648/j.ajrs.20160402.11
    AB  - The paper got the data of spectral reflectance of ground features from field surveying by using field spectroradiometer. The spatial distribution information of the ground features was obtained from the land-use map. Based on above mentioned, the generating algorithm of spectral reflectance image of ground features was developed by Modeler module of ERDAS Imaging software. The four bands were selected as example image bands, including the blue band (0.45-0.52ìm), the green band (0.52-0.60ìm), the red band (0.63-0.69ìm) and the infrared band (0.76-0.90ìm). The four band images with real geographical coordinates were generated from the spectral reflectance of ground features. In order to present the following images, the true color and the standard false color images were merged with four individual band images. By using the field spectroradiometer, relatively simple compared with hyperspectral imaging radiometer, the similar spectral reflectance images of ground features could be obtained with the secondary developed generating algorithm on the ERDAS Imaging software platform. Through the analysis of the spectral reflectance images of ground features, we can prove that the generated images are close to the real land scenes. Therefore, this paper provides a new idea and a new method for the first step of simulating remote sensing images with real geographic coordinates. Finally, the authors prefer to explain that further studies should be developed in two aspects. One issue is how to describe the spatial distributing information of ground features more accurately, and the other is how to differentiate the same class ground features with different spectral reflectance. Based on above, further more studies should include the effect of topographic factors on the spectral reflectance of ground features.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • School of Mineral Resources and Environmental Engineering, Shandong University of Technology, Zibo Shandong, China

  • State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

  • Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, USA

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