Super-resolution land cover mapping using a Markov random field based approach

• Published in: Remote sensing of environment 2005-06, Vol.96 (3), p.302-314
• Main Authors: Kasetkasem, Teerasit, Arora, Manoj K., Varshney, Pramod K.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Gibbs distribution; Internal geophysics; Land cover mapping; MAP classifier; Markov random fields; Remote sensing; Simulated annealing; Super-resolution land cover mapping; Teledetection and vegetation maps
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2005.02.006

Occurrence of mixed pixels in remote sensing images is a major problem particularly at coarse spatial resolutions. Therefore, sub-pixel classification is often preferred, where a pixel is resolved into various class components (also called class proportions or fractions). While, under most circumstances, land cover information in this form is more effective than crisp classification, sub-pixel classification fails to account for the spatial distribution of class proportions within the pixel. An alternative approach is to consider the spatial distribution of class proportions within and between pixels to perform super-resolution mapping (i.e. mapping land cover at a spatial resolution finer than the size of the pixel of the image). Markov random field (MRF) models are well suited to represent the spatial dependence within and between pixels. In this paper, an MRF model based approach is introduced to generate super-resolution land cover maps from remote sensing data. In the proposed MRF model based approach, the intensity values of pixels in a particular spatial structure (i.e., neighborhood) are allowed to have higher probability (i.e., weight) than others. Remote sensing images at two markedly different spatial resolutions, IKONOS MSS image at 4 m spatial resolution and Landsat ETM+ image at 30 m spatial resolution, are used to illustrate the effectiveness of the proposed MRF model based approach for super-resolution land cover mapping. The results show a significant increase in the accuracy of land cover maps at fine spatial resolution over that obtained from a recently proposed linear optimization approach suggested by Verhoeye and Wulf (2002) (Verhoeye, J., Wulf, R. D. (2002). Land Cover Mapping at Sub-pixel Scales using Linear Optimization Techniques. Remote Sensing of Environment, 79, 96–104).