Class Allocation for Soft-Then-Hard Subpixel Mapping Algorithms With Adaptive Visiting Order of Classes

The soft-then-hard subpixel mapping (STHSPM) algorithm is a type of subpixel mapping (SPM) algorithm consisting of soft class value (between 0 and 1) estimation and hard class allocation for each subpixel. This letter presents a new class allocation method for STHSPM algorithm. As an extension of our previous work in which subpixels for classes are decided in units of classes (UOC), the new approach, named adaptive UOC (AUOC), improves UOC with adaptive visiting order of classes. In AUOC, the visiting order of classes within each coarse pixel is determined based on the local structure rather than the global structure in UOC. Experiments on three remote sensing images show that AUOC is able to improve UOC in terms of SPM accuracy, particularly for SPM with small zoom factors.

[1]  Giles M. Foody,et al.  Impact of Land Cover Patch Size on the Accuracy of Patch Area Representation in HNN-Based Super Resolution Mapping , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Wenzhong Shi,et al.  Indicator Cokriging-Based Subpixel Land Cover Mapping With Shifted Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Giles M. Foody,et al.  Combining Pixel Swapping and Contouring Methods to Enhance Super-Resolution Mapping , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Danfeng Liu,et al.  Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery , 2012 .

[5]  Paul Scheunders,et al.  Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Peijun Li,et al.  A super-resolution mapping method using local indicator variograms , 2012 .

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

[8]  Alfred Stein,et al.  Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Liangpei Zhang,et al.  Adaptive Subpixel Mapping Based on a Multiagent System for Remote-Sensing Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

[13]  Wenzhong Shi,et al.  Utilizing Multiple Subpixel Shifted Images in Subpixel Mapping With Image Interpolation , 2014, IEEE Geoscience and Remote Sensing Letters.

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

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

[16]  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.

[17]  Liangpei Zhang,et al.  Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction , 2013, IEEE Transactions on Geoscience and Remote Sensing.

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

[19]  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.