Super-Resolution Land Cover Mapping Using Multiscale Self-Similarity Redundancy

Super-resolution mapping (SRM) is a method to estimate a fine-resolution land cover map from coarse-resolution fraction images. SRM is an ill-posed problem and regularization terms are always needed to be introduced to well-pose the solution. The regularization term based on the maximal spatial dependence has been widely used in SRM; however, it is often too simple to provide detailed land cover pattern information. In this paper, a novel SRM algorithm, which adopts a new regularization term that is generated using the multiscale self-similarity redundancy feature in fraction images, is proposed. Based on the multiscale self-similarity redundancy, the proposed SRM algorithm magnifies the input coarse-resolution fraction images to the images with the same spatial resolution as the final fine-resolution map to be estimated. A coarse-to-fine strategy is applied to insure the stability of the magnifying process. During the magnifying process, support vector regression is used to learn the relationship between image patches in fraction images and the down-sampled images, which is applied to estimate the magnified fraction images. The new regularization term is then constructed based on the final magnified fraction images and used to provide additional information for SRM. The proposed SRM algorithm was compared with popular SRM algorithms using both synthetic and real images. Experimental results show that the multiscale self-similarity redundancy feature widely exists in fraction images, and provides valuable land cover information for SRM. Resultant fine-resolution land cover maps generated by the proposed SRM algorithm have higher accuracy than other algorithms.

[1]  P. Atkinson.  Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery , 2005 .

[2]  Amandine Robin,et al.  Unsupervised Subpixelic Classification Using Coarse-Resolution Time Series and Structural Information , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Xia Wang,et al.  Spectral–spatial based sub-pixel mapping of remotely sensed imagery with multi-scale spatial dependence , 2015 .

[4]  Xiaodong Li,et al.  Land Cover Change Mapping at the Subpixel Scale With Different Spatial-Resolution Remotely Sensed Imagery , 2011, IEEE Geoscience and Remote Sensing Letters.

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

[6]  Fei Xiao,et al.  Superresolution Land Cover Mapping With Multiscale Information by Fusing Local Smoothness Prior and Downscaled Coarse Fractions , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Peter M. Atkinson,et al.  Mapping sub-pixel vector boundaries from remotely sensed images , 1996 .

[8]  Christopher A. Barnes,et al.  Completion of the 2006 National Land Cover Database for the conterminous United States. , 2011 .

[9]  Yong Ge,et al.  Development and Testing of a Subpixel Mapping Algorithm , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Peter M. Atkinson,et al.  Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study , 2009 .

[11]  Koen C. Mertens,et al.  A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models , 2006 .

[12]  Xiaodong Li,et al.  Super-resolution mapping based on the supervised fuzzy c-means approach , 2012 .

[13]  F. Ling,et al.  Waterline mapping at the subpixel scale from remote sensing imagery with high‐resolution digital elevation models , 2008 .

[14]  Liangpei Zhang,et al.  Sub-Pixel Mapping Based on Conditional Random Fields for Hyperspectral Remote Sensing Imagery , 2015, IEEE Journal of Selected Topics in Signal Processing.

[15]  Liguo Wang.  Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[16]  Pramod K. Varshney,et al.  Super-resolution land cover mapping using a Markov random field based approach , 2005 .

[17]  J. Wickham,et al.  Completion of the 2001 National Land Cover Database for the conterminous United States , 2007 .

[18]  Michael Elad,et al.  Example-based single document image super-resolution: a global MAP approach with outlier rejection , 2007, Multidimens. Syst. Signal Process..

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

[20]  Xiaodong Li,et al.  Example-Based Super-Resolution Land Cover Mapping Using Support Vector Regression , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Xiaodong Li,et al.  Spatially Adaptive Superresolution Land Cover Mapping With Multispectral and Panchromatic Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Yong Ge,et al.  Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Yong Ge,et al.  Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery , 2014 .

[24]  Liangpei Zhang,et al.  A new sub-pixel mapping algorithm based on a BP neural network with an observation model , 2008, Neurocomputing.

[25]  P. Atkinson,et al.  Sub-pixel mapping of remote sensing images based on radial basis function interpolation , 2014 .

[26]  Ye Zhang,et al.  Enhanced Self-Training Superresolution Mapping Technique for Hyperspectral Imagery , 2011, IEEE Geoscience and Remote Sensing Letters.

[27]  Xiaodong Li,et al.  Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm , 2015, IEEE Geoscience and Remote Sensing Letters.

[28]  G. M. Foody.  The role of soft classification techniques in the refinement of estimates of ground control point location , 2002 .

[29]  Lieven Verbeke,et al.  Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients , 2004 .

[30]  Xuelong Li,et al.  Single Image Super-Resolution With Multiscale Similarity Learning , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Fei Xiao,et al.  Subpixel Land Cover Mapping by Integrating Spectral and Spatial Information of Remotely Sensed Imagery , 2012, IEEE Geoscience and Remote Sensing Letters.

[32]  D. Roberts,et al.  Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil , 2007 .

[33]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[34]  Giles M. Foody,et al.  Super‐resolution mapping of the waterline from remotely sensed data , 2005 .

[35]  Liangpei Zhang,et al.  An Adaptive Subpixel Mapping Method Based on MAP Model and Class Determination Strategy for Hyperspectral Remote Sensing Imagery , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Xiaodong Li,et al.  A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images , 2014 .

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

[38]  M. Q. Nguyen,et al.  Superresolution mapping using a Hopfield neural network with lidar data , 2005, IEEE Geoscience and Remote Sensing Letters.

[39]  Xiaodong Li,et al.  Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices , 2011 .

[40]  Michal Irani,et al.  Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[41]  Yun Chen,et al.  DEM-based modification of pixel-swapping algorithm for enhancing floodplain inundation mapping , 2014 .

[42]  Fei Xiao,et al.  Interpolation-based super-resolution land cover mapping , 2013 .

[43]  Fei Xiao,et al.  Superresolution Land Cover Mapping Using Spatial Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Xiaodong Li,et al.  Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Michal Irani,et al.  Internal statistics of a single natural image , 2011, CVPR 2011.

[46]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[47]  Peter M. Atkinson,et al.  Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping , 2006 .

[48]  Wenzhong Shi,et al.  Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class , 2014, IEEE Transactions on Geoscience and Remote Sensing.