Super-Resolving Multiresolution Images With Band-Independent Geometry of Multispectral Pixels

A new resolution enhancement method is presented for multispectral and multiresolution images, such as those provided by the Sentinel-2 satellites. Starting from the highest resolution bands, band-dependent information (reflectance) is separated from information that is common to all bands (geometry of scene elements). This model is then applied to unmix low-resolution bands, preserving their reflectance, while propagating band-independent information to preserve the subpixel details. A reference implementation is provided, with an application example for super-resolving Sentinel-2 data.

[1]  Julien Radoux,et al.  Sentinel-2's Potential for Sub-Pixel Landscape Feature Detection , 2016, Remote. Sens..

[2]  K. Nikolakopoulos.  Comparison of Nine Fusion Techniques for Very High Resolution Data , 2008 .

[3]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

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

[5]  Lucien Wald,et al.  Quality of high resolution synthesised images: Is there a simple criterion ? , 2000 .

[6]  R. Molina,et al.  Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images , 2008 .

[7]  M. Körner,et al.  SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS , 2016 .

[8]  Antonio Turiel,et al.  Microcanonical multifractal formalism—a geometrical approach to multifractal systems: Part I. Singularity analysis , 2008 .

[9]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[11]  Bo Wu,et al.  Enhanced hyperspherical color space fusion technique preserving spectral and spatial content , 2015 .

[12]  A. Iwasaki,et al.  Super-resolution imaging using remote sensing platform , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[13]  Adam Finkelstein,et al.  PatchMatch: a randomized correspondence algorithm for structural image editing , 2009, SIGGRAPH 2009.

[14]  C. Padwick,et al.  WORLDVIEW-2 PAN-SHARPENING , 2010 .

[15]  Oriol Pont,et al.  Ocean Turbulent Dynamics at Superresolution From Optimal Multiresolution Analysis and Multiplicative Cascade , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Feng Li,et al.  Superresolution Reconstruction of Multispectral Data for Improved Image Classification , 2009, IEEE Geoscience and Remote Sensing Letters.

[17]  Li Chen,et al.  A soft MAP framework for blind super-resolution image reconstruction , 2009, Image Vis. Comput..

[18]  Kwang In Kim,et al.  Example-Based Learning for Single-Image Super-Resolution , 2008, DAGM-Symposium.

[19]  Alan R. Gillespie,et al.  Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques , 1987 .

[20]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  S. Sides,et al.  Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic , 1991 .

[22]  S. Baronti,et al.  Multispectral and panchromatic data fusion assessment without reference , 2008 .