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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5252
Title: A New Super Resolution Mapping Algorithm by Combining Pixel and Subpixel-Level Spatial Dependences With Colorimetry
Authors: Merugu S.
Jain, Kamal
Published in: Journal of the Indian Society of Remote Sensing
Abstract: Super resolution mapping is a continuously growing area of remote sensing. Satellite images coupled with a very high spectral resolution, and are suitable for detection and classification of surfaces and different elements in the observed image. The main problem with high resolution data for these applications is the (relatively) low spatial resolution, which can vary from a few to tens of meters. In the case of classification purposes, the major problem caused by low spatial resolution is related to subpixels, i.e., pixels in the image where more than one land cover class is within the same pixel. In such a case, the pixel cannot be considered as belonging to just one class, and the assignment of the pixel to a single class will inevitably lead to a loss of information, no matter what class is chosen. A new super resolution mapping (SRM) algorithm by combining pixel and subpixel-level spatial dependences with colorimetry is proposed in this paper. The pixel-level dependence is measured by the spatial attraction model with either surrounding or quadrant neighborhood, while the subpixel-level dependence is characterized by either the mean filter or the exponential weighting function. Both pixel-level and subpixel-level dependences are then fused as the weighted dependence for quickly obtaining the optimal spatial distribution of subpixels by employing the colorimetric algorithm. Synthetic imagery and a QuickBird image are tested for validation of the proposed method. The results demonstrate that the proposed method can achieve results with greater accuracy than two traditional subpixel mapping (SPM) methods and the mixed spatial attraction model method. Meanwhile, the proposed method needs considerably less computation time than the conventional mixed spatial attraction model method, and hence it provides a new solution to subpixel land cover mapping. ¬© 2014, Indian Society of Remote Sensing.
Citation: Journal of the Indian Society of Remote Sensing(2015), 43(2): 259-268
URI: https://doi.org/10.1007/s12524-014-0412-6
http://repository.iitr.ac.in/handle/123456789/5252
Issue Date: 2015
Publisher: Springer India
Keywords: Colorimetry
Spatial dependences and satellite imagery
Subpixel classification
Super resolution mapping
ISSN: 0255660X
Author Scopus IDs: 56658583800
56658294100
Author Affiliations: Merugu, S., Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Jain, K., Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Corresponding Author: Merugu, S.; Indian Institute of Technology RoorkeeIndia
Appears in Collections:Journal Publications [CE]

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