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Remote Sens. 2017, 9(4), 360; doi:10.3390/rs9040360

A New Spatial Attraction Model for Improving Subpixel Land Cover Classification

1
School of Earth Sciences, Zhejiang University, Hangzhou 310027, Zhejiang, China
2
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 14 January 2017 / Revised: 1 April 2017 / Accepted: 7 April 2017 / Published: 11 April 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [3985 KB, uploaded 12 April 2017]   |  

Abstract

Subpixel mapping (SPM) is a technique that produces hard classification maps at a spatial resolution finer than that of the input images produced when handling mixed pixels. Existing spatial attraction model (SAM) techniques have been proven to be an effective SPM method. The techniques mostly differ in the way in which they compute the spatial attraction, for example, from the surrounding pixels in the subpixel/pixel spatial attraction model (SPSAM), from the subpixels within the surrounding pixels in the modified SPSAM (MSPSAM), or from the subpixels within the surrounding pixels and the touching subpixels within the central pixel in the mixed spatial attraction model (MSAM). However, they have a number of common defects, such as a lack of consideration of the attraction from subpixels within the central pixel and the unequal treatment of attraction from surrounding subpixels of the same distance. In order to overcome these defects, this study proposed an improved SAM (ISAM) for SPM. ISAM estimates the attraction value of the current subpixel at the center of a moving window from all subpixels within the window, and moves the window one subpixel per step. Experimental results from both Landsat and MODIS imagery have proven that ISAM, when compared with other SAMs, can improve SPM accuracies and is a more efficient SPM technique than MSPSAM and MSAM. View Full-Text
Keywords: spatial attraction model (SAM); subpixel mapping (SPM); land cover; mixed pixel; spatial distribution; hard classification spatial attraction model (SAM); subpixel mapping (SPM); land cover; mixed pixel; spatial distribution; hard classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, L.; Huang, Y.; Di, L.; Hang, D. A New Spatial Attraction Model for Improving Subpixel Land Cover Classification. Remote Sens. 2017, 9, 360.

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