Journal ArticleDOI
Utilizing Multiple Subpixel Shifted Images in Subpixel Mapping With Image Interpolation
Qunming Wang,Wenzhong Shi +1 more
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TLDR
In this letter, multiple subpixel shifted images (MSIs) were utilized to increase the accuracy of subpixel mapping (SPM), based on the fast bilinear and bicubic interpolation.Abstract:
In this letter, multiple subpixel shifted images (MSIs) were utilized to increase the accuracy of subpixel mapping (SPM), based on the fast bilinear and bicubic interpolation. First, each coarse spatial resolution image of MSI is soft classified to obtain class fraction images. Using bilinear or bicubic interpolation, all fraction images of MSI are upsampled to the desired fine spatial resolution. The multiple fine spatial resolution images for each class are then integrated. Finally, the integrated fine spatial resolution images are used to allocate hard class labels to subpixels. Experiments on two remote sensing images showed that, with MSI, both bilinear and bicubic interpolation-based SPMs are more accurate. The new methods are fast and do not need any prior spatial structure information.read more
Citations
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Journal ArticleDOI
Sub-pixel mapping of remote sensing images based on radial basis function interpolation
TL;DR: In this article, a new sub-pixel mapping (SPM) method based on radial basis function interpolation is proposed for land cover mapping at the sub-pixels scale.
Journal ArticleDOI
Land Cover Change Detection at Subpixel Resolution With a Hopfield Neural Network
TL;DR: It was found that the proposed HNN with an FSRM method can separate more real changes from noise and produce more accurate LCCD results than the state-of-the-art methods.
Journal ArticleDOI
A MAP-Based Approach for Hyperspectral Imagery Super-Resolution
TL;DR: A novel single image Bayesian super-resolution algorithm where the hyperspectral image (HSI) is the only source of information is proposed and it is shown that the proposed method outperforms the state of the art methods in terms of quality while preserving the spectral consistency.
Journal ArticleDOI
Example-Based Super-Resolution Land Cover Mapping Using Support Vector Regression
TL;DR: An example-based SRM model using support vector regression (SVR_SRM), which can generate fine resolution land cover maps with more detailed spatial information and higher accuracy at different spatial scales is proposed.
Journal ArticleDOI
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.
References
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Journal ArticleDOI
Super-resolution target identification from remotely sensed images using a Hopfield neural network
TL;DR: The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated, and the resultant maps provided an accurate and improved representation of the land covers studied.
Journal ArticleDOI
Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction
TL;DR: A tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification are defined.
Journal ArticleDOI
Super-resolution land cover mapping using a Markov random field based approach
TL;DR: 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).
Journal ArticleDOI
Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery
TL;DR: In this article, a simple, efficient algorithm is presented for sub-pixel target mapping from remotely-sensed images, which works in a series of iterations, each of which contains three stages.
Journal ArticleDOI
Using genetic algorithms in sub-pixel mapping
TL;DR: Sub-pixel mapping is a technique designed to use the information contained in these mixed pixels to obtain a sharpened image using genetic algorithms combined with the assumption of spatial dependence to assign a location to every sub-pixel.
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