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

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.

[1]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[2]  G. Foody.  Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution , 1998 .

[3]  M. Gormish,et al.  Multiscale Sharpening and Smoothing in Besov Spaces with Applications to Image Enhancement , 2001 .

[4]  Justin K. Romberg,et al.  Bayesian tree-structured image modeling using wavelet-domain hidden Markov models , 2001, IEEE Trans. Image Process..

[5]  L. Wald,et al.  Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation , 2000 .

[6]  Zarine P. Kemp,et al.  Innovations in GIS 4 , 1997 .

[7]  Lei Zhang,et al.  Hybrid inter- and intra-wavelet scale image restoration , 2003, Pattern Recognit..

[8]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[9]  F. J. A. López,et al.  Restoring SPOT images using PSF-derived deconvolution filters , 2002 .

[10]  Jorge Núñez,et al.  Astronomical image segmentation by self-organizing neural networks and wavelets , 2003, Neural Networks.

[11]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[12]  Paul Aplin,et al.  Sub-pixel land cover mapping for per-field classification , 2001 .

[13]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[14]  Frieke Vancoillie.  Design and application of artificial neural networks for digital image classification of tropical savanna vegetation , 2003 .

[15]  P. Atkinson,et al.  Mapping sub-pixel proportional land cover with AVHRR imagery , 1997 .

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[18]  John R. Schott,et al.  Application of Spectral Mixture Analysis and Image Fusion Techniques for Image Sharpening , 1998 .

[19]  Adam Krzyzak,et al.  Contour-based handwritten numeral recognition using multiwavelets and neural networks , 2003, Pattern Recognit..

[20]  L. Bian.  Retrieving urban objects using a wavelet transform approach , 2003 .

[21]  Robert De Wulf,et al.  Sub-pixel mapping with neural networks : real-world spatial configurations learned from artificial shapes , 2003 .

[22]  Hugh G. Lewis,et al.  Super-resolution land cover pattern prediction using a Hopfield neural network , 2002 .

[23]  K. K. Mohanty.  The wavelet transform for local image enhancement , 1997 .

[24]  Robert De Wulf,et al.  Land cover mapping at sub-pixel scales using linear optimization techniques , 2002 .

[25]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[26]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Hugh G. Lewis,et al.  Super-resolution target identification from remotely sensed images using a Hopfield neural network , 2001, IEEE Trans. Geosci. Remote. Sens..

[28]  Dhiraj K. Pradhan,et al.  A new class of bit- and byte-error control codes , 1992, IEEE Trans. Inf. Theory.

[29]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  A. Strahler,et al.  Artificial neural network response to mixed pixels in coarse-resolution satellite data , 1996 .

[31]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[32]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[33]  L. P. C. Verbeke,et al.  Using genetic algorithms in sub-pixel mapping , 2003 .

[34]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[35]  Luciano Alparone,et al.  Image fusion—the ARSIS concept and some successful implementation schemes , 2003 .