Super-resolution mapping (SRM) is a recently developed research task in the field of remotely sensed information processing. It provides the ability to obtain land-cover maps at a finer scale using relatively low-resolution images. Existing algorithms based on indicator geostatistics
and downscaling cokriging offer an SRM approach using spatial structure models derived from real data. In this article, a novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model. In the SLIV method, indicator variograms extracted from
target-resolution classification are produced from a representative local area as opposed to using the entire image. This simplifies the application of the method since limited target-resolution reference data are required. Our investigation on three diverse case studies shows that the local
window (approximately 2% of the entire study area) selection process offers comparable accuracy results to those using globally derived spatial structures, indicating our methodology to be a promising practice. Furthermore, comparison of the proposed method with random realizations indicates
an improvement of 7–12% in terms of overall accuracy and 15–18% in terms of the kappa coefficient. The evaluation focused on a 270–30 m pixel size reconstruction as a potential popular application, for example moving from Moderate Resolution Imaging Spectroradiometer (MODIS)
to Landsat-type resolutions.
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Document Type: Research Article
Department of Environmental Resources Engineering,State University of New York College of Environmental Science and Forestry, Syracuse,NY,13210, USA
Institute of Remote Sensing and GIS, Peking University, Beijing,100871, PR China
Publication date: 2012-12-20
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