Journal Article10.1080/01431161.2012.702234
A super-resolution mapping method using local indicator variograms
TL;DR: A novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model that offers comparable accuracy results to those using globally derived spatial structures, indicating the methodology to be a promising practice.
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Abstract: 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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Citations
Super-Resolution Mapping Based on Spatial–Spectral Correlation for Spectral Imagery
TL;DR: Experimental results show that the proposed SSC yields better mapping results than state-of-the-art methods, and the utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors.
165
Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class
TL;DR: UOC provides an effective and real-time class allocation method for STHSPM algorithms, which allocates classes in units of class (UOC) and is able to produce higher SPM accuracy than UOS and HAVF.
Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information
TL;DR: The proposed method extends ICK to cases where the prior spatial structure information is unavailable, and obtains comparable SPM accuracy to ICK that requires semivariogram estimated from fine spatial resolution training images.
54
CNN based sub-pixel mapping for hyperspectral images
TL;DR: Experiments indicate that the joint optimization of spectral unmixing and sub-pixel mapping stages improves the accuracy as well as the convergence time, and the proposed LSTM approach gives better results, especially for linear mixtures, in comparison with the encoder–decoder based approaches.
53
Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection
TL;DR: This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): the fine spatialresolution land cover maps (FRMs) are predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results.
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