Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

• Published in: Remote sensing of environment Vol. 91; no. 2; pp. 225 - 236
• Main Authors: Mertens, Koen C., Verbeke, Lieven P.C., Westra, Toon, De Wulf, Robert R.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 30.05.2004; Elsevier Science
• Subjects: Applied geophysics; Areal geology. Maps; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geologic maps, cartography; Internal geophysics; Neural networks; Resolution; Sub-pixel mapping; Sub-pixel sharpening; Wavelets; Belgium; Western Europe; Sub-pixel mapping; Wavelets; Neural networks; Sub-pixel sharpening; Resolution; algorithms; maps; Spot; Europe; Space remote sensing; neural networks; accuracy; cartography; classification; image analysis; imagery; spatial resolution; Pixel; high resolution
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2004.03.003

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.