Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Science and Engineering
      • Science and Engineering Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Science and Engineering
      • Science and Engineering Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Image data fusion for the remote sensing of freshwater environments

      Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J.
      DOI
       10.1016/j.apgeog.2011.07.010
      Link
       www.sciencedirect.com
      Find in your library  
      Citation
      Export citation
      Ashraf, S., Brabyn, L. & Hicks, B.J. (2011). Image data fusion for the remote sensing of freshwater environments. Applied Geography, 32(2), 619-628.
      Permanent Research Commons link: https://hdl.handle.net/10289/5666
      Abstract
      Remote sensing based mapping of diverse and heterogeneous freshwater environments requires high-resolution images. Data fusion is a useful technique for producing a high-resolution multispectral image from the merging of a high-resolution panchromatic image with a low-resolution multispectral image. Given the increasing availability of images from different satellite sensors that have different spectral and spatial resolutions, data fusion techniques that combine the strengths of different images will be increasingly important to Geography for land-cover mapping. Different data fusion methods however, add spectral and spatial distortions to the resultant data depending on the geographical context; therefore a careful selection of the fusion method is required. This paper compares a technique called subtractive resolution merge, which has not previously been formally tested, with conventional techniques such as Brovey transformation, principal component substitution, local mean and variance matching, and optimised high pass filter addition. Data fusion techniques are grouped into spectral and spatial centric methods. Subtractive resolution merge belongs to a new class of data fusion techniques that uses a mix of both spatial and spectral centric approaches. The different data fusion techniques were applied to a QuickBird image of a semi-aquatic freshwater environment in New Zealand. The results were compared both qualitatively and quantitatively using spectral and spatial error metrics. This research concludes that subtractive resolution merge performed better than all the other techniques and will be a valuable technique for enhancing images for freshwater land-cover mapping.
      Date
      2011
      Type
      Journal Article
      Publisher
      Elsevier
      Collections
      • Science and Engineering Papers [3122]
      • Arts and Social Sciences Papers [1423]
      Show full item record  

      Usage

       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement