Sub-pixel Mapping Method Research Articles

Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery

This article presents a vectorial boundary-based sub-pixel mapping (VBSPM) method to obtain the land-cover distribution with finer spatial resolution in mixed pixels. With inheritance from the geometric SPM (GSPM), VBSPM first geometrically partitions a mixed pixel using polygons, and then utilizes a vectorial boundary extraction model (VBEM), rather than the rasterization method in GSPM, to determine the location and length of each edge in the polygon, while these edges are located at the boundary of and within the interior of the mixed pixel. Furthermore, VBSPM uses a decay function to manage the mixed pixels along the image boundary region due to the missing parts of their neighbours. Finally, a ray-crossing algorithm is employed to determine the land-cover class of each sub-pixel in terms of vectorial boundaries. The experiments with artificial and remotely sensed images have demonstrated that VBSPM can reduce the inconsistency between the boundaries of different land-cover classes, approximately calculating errors with an odd zoom factor, and achieve more accurate sub-pixel mapping results than the hard classification methods and GSPM.

A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images

Sub-pixel mapping is a technique designed to obtain the spatial distribution of different classes in mixed pixels at the sub-pixel scale by transforming fraction images into a classification map. Traditional sub-pixel mapping algorithms only utilize a low-resolution image, and sub-pixel mapping is an ill-posed problem as information in a single low-resolution image is not enough to obtain a high-resolution land-cover map. The accuracy can be improved by incorporating auxiliary datasets to provide more land-cover information, such as multiple shifted images from the same area. In this paper, an attraction model is used to utilize multiple shifted remotely sensed images which have complementary information to each other at the sub-pixel scale. For multiple shifted images, one is selected as the base image, and the shifts of the other images and the base image can be calculated. The improved attraction model can obtain the impacts of the base image and auxiliary images, respectively, and integrate them to achieve a better result. The proposed algorithm was tested on synthetic real imagery, and the experimental results demonstrate that the proposed approach outperforms two traditional sub-pixel mapping algorithms based on a single image, and the another multiple shifted images based sub-pixel mapping method.

Indicator Cokriging-Based Subpixel Land Cover Mapping With Shifted Images

Subpixel mapping (SPM) is a technique for predicting the spatial distribution of land cover classes in remote sensing images at a finer spatial resolution level than those of the input images. Indicator cokriging (ICK) has been found to be an effective and efficient SPM method. The accuracy of this model, however, is limited by insufficient constraints. In this paper, the accuracy of the ICK-based SPM model is enhanced by using additional information gained from multiple shifted images (MSIs). First, each shifted image is utilized to compute the conditional probability of class occurrence at any fine spatial resolution pixel (i.e., subpixel) using ICK, and a set of conditional probability maps for all classes are generated for each image. The multiple ICK-derived conditional probability maps are then integrated, according to the estimated subpixel shifts of MSI. Lastly, class allocation at the subpixel scale is implemented to produce SPM results. The proposed algorithm was tested on two synthetic coarse spatial resolution remote sensing images and a set of real Moderate Resolution Imaging Spectroradiometer (MODIS) data. It was evaluated both visually and quantitatively. The experimental results showed that more accurate SPM results can be generated with MSI than with a single observed coarse image in conventional ICK-based SPM. In addition, the accuracy of the proposed method is higher than that of the existing Hopfield neural network (HNN)-based SPM and the HNN with MSI.

Edge-directed interpolation-based sub-pixel mapping

Sub-pixel mapping is a technology to obtain the spatial distribution information of land cover within mixed pixels. In this letter, two kinds of edge-directed interpolation algorithms are proposed. These algorithms are applied to coarse spatial resolution images to obtain a high-resolution image with probability information. Then hard classification on a per sub-pixel basis is implemented to achieve sub-pixel mapping. Both methods are demonstrated with three synthetic images. Experiments show that the proposed sub-pixel mapping methods can retain image edge feature better and produce higher accuracy.

Attraction-Repulsion Model-Based Subpixel Mapping of Multi-/Hyperspectral Imagery

This paper presents a new subpixel mapping method based on subpixel attraction-repulsion. The proposed method is formulated as an optimization problem with respect to attraction-repulsion among subpixels and is used to reconstruct a finer spatial resolution image from a lower resolution one. A comprehensive experiment is conducted to demonstrate the performance of the proposed method, by comparing it with the other three existing subpixel mapping methods, i.e., linear optimization, pixel swapping and spatial attraction model methods. In the experiment, both a synthetic image with known fractional abundances and an EO-1 Hyperion hyperspectral image of Shanghai were used to evaluate performances of the subpixel mapping methods. The experimental result shows that by using spatial dependence with attraction between the same types of ground objects and repulsion between different types of these objects, the proposed subpixel mapping method achieves a better performance on subpixel mapping than the other three methods.

Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images

Subpixel mapping (SPM) is a promising technique to increase the spatial resolution of land cover maps. Markov random field (MRF)-based SPM has the advantages of considering spatial and spectral constraints simultaneously. In the conventional MRF, only the spectral information of one observed coarse spatial resolution image is utilized, which limits the SPM accuracy. In this letter, supplementary information from subpixel shifted remote sensing images (SSRSI) is used with MRF to produce more accurate SPM results. That is, spectral information from SSRSI is incorporated into the likelihood energy function of MRF to provide multiple spectral constraints. Simulated and real images were tested with the subpixel/pixel spatial attraction model, Hopfield neural networks (HNNs), HNN with SSRSI, image interpolation then hard classification, conventional MRF, and proposed MRF with SSRSI based SPM methods. Results showed that the proposed method can generate the most accurate SPM results among these methods.

Sub-Pixel Mapping Based on a MAP Model With Multiple Shifted Hyperspectral Imagery

Sub-pixel mapping is technique used to obtain the spatial distribution of different classes at the sub-pixel scale by transforming fraction images to a classification map with a higher resolution. Traditional sub-pixel mapping algorithms only utilize a low-resolution image, the information of which is not enough to obtain a high-resolution land-cover map. The accuracy of sub-pixel mapping can be improved by incorporating auxiliary datasets, such as multiple shifted images in the same area, to provide more sub-pixel land-cover information. In this paper, a sub-pixel mapping framework based on a maximum a posteriori (MAP) model is proposed to utilize the complementary information of multiple shifted images. In the proposed framework, the sub-pixel mapping problem is transformed to a regularization problem, and the MAP model is used to regularize the sub-pixel mapping problem to be well-posed by adding some prior information, such as a Laplacian model. The proposed algorithm was compared with a traditional sub-pixel mapping algorithm based on a single image, and another multiple shifted images based sub-pixel mapping method, using both synthetic and real hyperspectral images. Experimental results demonstrated that the proposed approach outperforms the traditional sub-pixel mapping algorithms, and hence provides an effective option to improve the accuracy of sub-pixel mapping for hyperspectral imagery.

Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation

Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/class. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensed image from Landsat TM 30m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images.

Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery

Sub-pixel mapping (SPM) is a promising method to predict the spatial locations of land cover classes at the sub-pixel scale for remotely sensed imagery, using the fraction images generated by soft classification as input. At present, SPM treats all sub-pixels of different land cover classes in the same strategy by maximizing their spatial dependence. Although the maximal spatial dependence is a simple method to describe the spatial pattern of land cover classes and has been proved to be an effective principle for SPM, it does not reflect real-world situations. Given that spatial patterns are land cover class- or object-specific, each land cover class or object should be designated its own specific spatial pattern description when SPM is applied. In this paper, a novel object-based sub-pixel mapping (OBSPM) method was proposed to map buildings at the sub-pixel scale. On the basis of the prior information of the building shape (i.e., the building boundaries are parallel or perpendicular to the main orientation), a novel anisotropic spatial dependence model is adopted in the SPM procedure. The proposed OBSPM model includes three main steps: building segmentation, building feature extraction, and anisotropic SPM of buildings. The proposed model is evaluated with a simulated synthetic image and an actual AVIRIS image. The results show that OBSPM obtains more accurate building maps than do conventional SPM models, and the accuracy of fraction images and the spatial resolutions of remotely sensed images are two crucial factors that influence the OBSPM results. Furthermore, extending the OBSPM model to more land cover classes to incorporate more specific prior information is a promising method in enhancing the applicability of SPM to practical situations.

Progress on Sub-pixel Mapping Methods for Remotely Sensed Images

Mixed pixels commonly exist in remote sensing images.They may cause difficulties to process and analyze the remote sensing images.Sub-pixel mapping method is used to solve the problem of mixed pixels.It can obtain more accurate results for image classification and more details of distribution of land cover classes.There are always three steps to preform the sub-pixel mapping:① unmixing the mixed pixel;② obtaining the soft information;③ mapping at sub-pixel scale.This paper described the details about the sub-pixel mapping method in recent years and reviewed the development of these sub-pixel mapping methods.These methods can be classified into four types:① Spatial correlation-based sub-pixel mapping;② spatial construct information-based sub-pixel mapping;③ article neural network-based sub-pixel mapping;④ pixel swapping-based sub-pixel mapping.Then these methods are contrasted and analyzed as well.Finally,a trend for development of sub-pixel mapping is given in the conclusion.

Sub-pixel mapping method based on ANN and super-resolution reconstructed model

Sub-pixel mapping method based on ANN and super-resolution reconstructed model

Subpixel mapping on remote sensing imagery using a prediction model combining wavelet transform and radial basis function neural network

Soft classification methods can be used for mixed-pixel classification on remote sensing imagery by estimating different land cover class fractions of every pixel. However, the spatial distribution and location of these class components within the pixel remain unknown. To map land cover at subpixel scale and increase the spatial resolution of land cover classification maps, in this paper, a prediction model combining wavelet transform and Radial Basis Functions (RBF) neural network, abbreviated as Wavelet-RBFNN, is constructed by predicting high-frequency wavelet coefficients from low-frequency coefficients at the same resolution with RBF network and taking wavelet coefficients at coarser resolution as training samples. According to different land cover class fraction images obtained from mixed-pixel classification, based on the assumption of neighborhood dependence of wavelet coefficients, subpixel mapping on remote sensing imagery can be accomplished through two steps, i.e., prediction of land cover class compositions within subpixels and hard classification. The experimental results obtained with artificial images, QuickBird image and Landsat 7 ETM+ image indicate that the subpixel mapping method proposed in this paper can successfully produce super-resolution land cover classification maps from remote sensing imagery, outperforming cubic B-spline and Kriging interpolation method in visual effect and prediction accuracy. The Wavelet-RBFNN model can also be applied to simulate higher spatial resolution image, and automatically identify and locate land cover targets at the subpixel scales, when the cost and availability of high resolution imagery prohibit its use in many areas of work.