Using low-spectral-resolution images to acquire simulated hyperspectral images

  • Authors:

  • Fang Chen;Zheng Niu;GenYun Sun;ChangYao Wang;Jack Teng

  • Affiliations:

  • State Key Lab. of Remote Sensing Sci., (J. Spon.) by the Inst. of Rem. Sensing Apps. of Ch. Acad. of Sci. and Beijing Normal Univ., Beijing 100101, China, Grad. Sch. of the Ch. Acad. of Sci., Beij ...;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, Chin ...;State Key Laboratory of Remote Sensing Sci., (J. Spon.) Inst. of Remote Sensing Apps. of Ch. Acad. of Sci. and Beijing Normal Univ., Beijing 100101, China, Grad. Sch. of the Ch. Acad. of Sci., Bei ...;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, Chin ...;Resource Management and Environmental Sciences, University of British Columbia, Canada

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2008

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Abstract

We propose a method to acquire simulated hyperspectral images using low-spectral-resolution images. Hyperspectral images provide more spectral information than low-spectral-resolution images, because of the additional spectral bands used for data acquisition in hyperspectral imaging. Unfortunately, original hyperspectral images are more expensive and more difficult to acquire. However, some research questions require an abundance of spectral information for ground monitoring, which original hyperspectral images can easily provide. Hence, we need to propose a method to acquire simulated hyperspectral images, when original hyperspectral images are especially necessary. Since low-spectral-resolution images are readily available and cheaper, we develop a method to acquire simulated hyperspectral images using low-spectral-resolution images. With simulated hyperspectral images, we can acquire more 'hidden' information from low-spectral-resolution images. Our method uses the principles of pixel-mixing to understand the compositional relationship of spectrum data to an image pixel, and to simulate radiation transmission processes. To this end, we use previously obtained data (i.e. spectrum library) and the sorting data of objects that are derived from a low-spectral-resolution image. Using the simulation of radiation transmission processes and these different data, we acquire simulated hyperspectral images. In addition, previous analyses of simulated remotely sensed images do not use quantitative statistical measures, but use qualitative methods, describing simulated images by sight. Here, we quantitatively assess our simulation by comparing the correlation coefficients of simulated images and real images. Finally, we use simulated hyperspectral images, real Hyperion images, and their corresponding ALI images to generate several classification images. The classification results demonstrate that simulated hyperspectral data contain additional information not available in the multispectral data. We find that our method can acquire simulated hyperspectral images quickly.