Super-resolution Mapping Algorithms Research Articles

Super-resolution for mapping the debris-covered glaciers, central Himalaya, India

New techniques are being implemented to precisely monitor the changes in debris-covered glaciers for ascertaining their response to climate change. The super-resolution mapping (SRM) technique is implemented here to provide the low-cost and fine-resolution facies maps of debris-covered glaciers in the Indian Himalayan region using coarse-resolution satellite data. The implemented SRM algorithm is capable of preserving the spatial pattern of facies on the debris-covered glacier surface. Scale factor (sf) and surface heterogeneity are two possible determinants of SRM accuracy. Larger sfs decrease the SRM accuracy and its computing efficiency, and hence optimal sfs must be selected. Higher SRM accuracies are obtained for less heterogenic glacier surfaces and vice-versa. Uncertainty in the resultant maps arises due to the underlying natural factors (shadow/clouds) and seasonal differences in the image acquisition. Temporal implementation of SRM is feasible to assess the facies variations on a debris-covered glacier surface, facilitating time- and cost-effective monitoring of its response to climate change over large areas.

A genetic algorithm based approach to estimate the volume of a drinking water reservoir in Chennai city, South India, using multi-spectral satellite images

This paper is concerned with the use of satellite image processing and remote sensing approaches to determine the water-spread area and capacity of a drinking water reservoir in Poondi, near Chennai City, south India. Estimation of water-spread area and volume of the reservoirs using traditional methods like field surveys, acoustic surveys and hydrographic surveys is time-consuming, laborious and expensive. To overcome these problems, many researchers have used satellite images and the per-pixel (hard classification), the sub-pixel (soft classification) and the super resolution mapping approaches. The conventional hard and soft classification approaches do not give accurate results due to the presence of mixed pixels in the image scene of the periphery of the reservoir. In this work, a per-pixel algorithm (Maximum Likelihood), a sub-pixel algorithm (Fuzzy C Means) and a super resolution mapping algorithm (Genetic algorithm) are developed and the water-spread area is estimated for the reservoir using multi-date Landsat8 OLI images. The volume of the reservoir at different water levels are estimated using the water-spread area and the Trapezoidal formula. The capacity estimated from satellite image-derived area is compared with the capacity data obtained from the Poondi reservoir authority. The error in estimation due to the per-pixel approach is 16.74%, while it is 9.06% for the sub-pixel approach and a mere 3.25% for the super-resolution approach. Thus, the super-resolution mapping approach results in minimum error when compared to the sub-pixel approach which in turn gives lesser error result when comparing with the per-pixel approach.

Utilizing Pansharpening Technique to Produce Sub-Pixel Resolution Thematic Map from Coarse Remote Sensing Image

Super-resolution mapping (SRM) is a technique to obtain sub-pixel resolution thematic map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (MAP) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset.

Reconstruction of River Boundaries at Sub-Pixel Resolution: Estimation and Spatial Allocation of Water Fractions

Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for the normalized difference water index (OBA-NDWI) is proposed for identifying the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) algorithm and interpolation-based algorithms are also applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2 = 0.9, RMSE = 7% for eight-band WorldView-3 (WV-3) image and R2 = 0.87, RMSE = 9% for GeoEye image). The spectral bands of WV-3 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas by about 10% with respect to conventional hard classification.

An Iterative Interpolation Deconvolution Algorithm for Superresolution Land Cover Mapping

Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms.

Learning-Based Superresolution Land Cover Mapping

Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.

Super-Resolution Land Cover Mapping Using Multiscale Self-Similarity Redundancy

Super-resolution mapping (SRM) is a method to estimate a fine-resolution land cover map from coarse-resolution fraction images. SRM is an ill-posed problem and regularization terms are always needed to be introduced to well-pose the solution. The regularization term based on the maximal spatial dependence has been widely used in SRM; however, it is often too simple to provide detailed land cover pattern information. In this paper, a novel SRM algorithm, which adopts a new regularization term that is generated using the multiscale self-similarity redundancy feature in fraction images, is proposed. Based on the multiscale self-similarity redundancy, the proposed SRM algorithm magnifies the input coarse-resolution fraction images to the images with the same spatial resolution as the final fine-resolution map to be estimated. A coarse-to-fine strategy is applied to insure the stability of the magnifying process. During the magnifying process, support vector regression is used to learn the relationship between image patches in fraction images and the down-sampled images, which is applied to estimate the magnified fraction images. The new regularization term is then constructed based on the final magnified fraction images and used to provide additional information for SRM. The proposed SRM algorithm was compared with popular SRM algorithms using both synthetic and real images. Experimental results show that the multiscale self-similarity redundancy feature widely exists in fraction images, and provides valuable land cover information for SRM. Resultant fine-resolution land cover maps generated by the proposed SRM algorithm have higher accuracy than other algorithms.

Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm

Super-resolution mapping (SRM) is a method for generating a fine-resolution land cover map from coarse-resolution fraction images. Example-regression-based SRM algorithms can estimate a fine-resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated fine-resolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.

Super-Resolution Land Cover Mapping Based on Multiscale Spatial Regularization

Super-resolution mapping (SRM) is a method for allocating land cover classes at a fine scale according to coarse fraction images. Based on a spatial regularization framework, this paper proposes a new regularization method for SRM that integrates multiscale spatial information from the fine scale as a smooth term and from the coarse scale as a penalty term. The smooth term is considered a homogeneity constraint, and the penalty term is used to characterize the heterogeneity constraint. Specifically, the smooth term depends on the local fine scale spatial consistency, and is used to smooth edges and eliminate speckle points. The penalty term depends on the coarse scale local spatial differences, and suppresses the over-smoothing effect from the fine scale information while preserving more details (e.g., connectivity and aggregation of linear land cover patterns). We validated our method using simulated and synthetic images, and compared the results to four representative SRM algorithms. Our numerical experiments demonstrated that the proposed method can produce more accurate maps, reduce differences in the number of patches, visually preserve smoother edges and more details, reject speckle points, and suppress over-smoothing.

A New Super Resolution Mapping Algorithm by Combining Pixel and Subpixel-Level Spatial Dependences With Colorimetry

Super resolution mapping is a continuously growing area of remote sensing. Satellite images coupled with a very high spectral resolution, and are suitable for detection and classification of surfaces and different elements in the observed image. The main problem with high resolution data for these applications is the (relatively) low spatial resolution, which can vary from a few to tens of meters. In the case of classification purposes, the major problem caused by low spatial resolution is related to subpixels, i.e., pixels in the image where more than one land cover class is within the same pixel. In such a case, the pixel cannot be considered as belonging to just one class, and the assignment of the pixel to a single class will inevitably lead to a loss of information, no matter what class is chosen. A new super resolution mapping (SRM) algorithm by combining pixel and subpixel-level spatial dependences with colorimetry is proposed in this paper. The pixel-level dependence is measured by the spatial attraction model with either surrounding or quadrant neighborhood, while the subpixel-level dependence is characterized by either the mean filter or the exponential weighting function. Both pixel-level and subpixel-level dependences are then fused as the weighted dependence for quickly obtaining the optimal spatial distribution of subpixels by employing the colorimetric algorithm. Synthetic imagery and a QuickBird image are tested for validation of the proposed method. The results demonstrate that the proposed method can achieve results with greater accuracy than two traditional subpixel mapping (SPM) methods and the mixed spatial attraction model method. Meanwhile, the proposed method needs considerably less computation time than the conventional mixed spatial attraction model method, and hence it provides a new solution to subpixel land cover mapping.

Superresolution Land Cover Mapping With Multiscale Information by Fusing Local Smoothness Prior and Downscaled Coarse Fractions

Superresolution mapping (SRM) is a technique for translating original coarse-resolution fractions into a fine-resolution land cover map by dividing a coarse-resolution pixel into a few finer resolution pixels and determining their class labels. SRM can be solved by considering it a maximum a posteriori principle-based classification problem and by assigning each fine pixel as the class with the highest probability. Fine-pixel class membership probabilities (CMPs) can be calculated at two different scales: at the fine scale, in which the target fine pixel is compared with other fine pixels, and at the coarse scale, in which coarse fractions are downscaled into fine-pixel probabilities. The fine-scale CMP is suitable for representing local land cover features but not for maintaining global features. The coarse-scale CMP is the opposite of the fine-scale CMP. This paper proposes a novel multiscale approach to overcome this shortcoming by fusing the CMP calculated at both fine and coarse scales with the tau model. With the fused CMP, a simulated-annealing algorithm is applied to produce a fine-resolution land cover map. The land cover maps generated from QuickBird and IKONOS images and the National Land Cover Database were used to validate the effectiveness of the proposed SRM algorithms. The proposed SRM algorithms were evaluated visually and quantitatively by comparing them with several existing SRM algorithms. The results indicate that the accuracy of land cover maps at fine spatial resolution increased significantly compared with that obtained from all existing SRM algorithms.

Super-Resolution Land Cover Mapping with Spatial–Temporal Dependence by Integrating a Former Fine Resolution Map

Super-resolution mapping (SRM) is a technique to predict spatial locations of land cover classes at the subpixel scale within coarse resolution remotely sensed image pixels. Due to the lack of information about the spatial pattern of land covers, uncertainty always exists in resultant fine-resolution land cover maps. In the present work, by integrating a former fine-resolution land cover map, the spatial dependence used in existing SRM algorithms is extended into a novel spatial-temporal dependence used in the SRM algorithm (SRM_STD). The spatial-temporal dependence consists of the spatial dependences of former fine-resolution land cover map, the spatial dependences of latter coarse resolution fraction images, and the corresponding dependence between former and latter land cover maps. By considering the spatial-temporal dependences of subpixels, SRM_STD can inherit valuable land cover information from the former fine-resolution land cover map, and reduce the uncertainty of SRM to a large extent. The performance of the proposed SRM_STD algorithm is assessed using a subset of the National Land Cover Database datasets and land cover maps produced by Landsat imagery in an area of rapid urban expansion. The results of two experiments show that the former dependence has little influence on the result, whereas the corresponding dependence plays a crucial role on the result. With a large weight of corresponding dependence, the proposed SRM_STD algorithm can produce fine-resolution land cover maps with higher accuracy than those of hard classification and the pixel swapping algorithm.

Example-Based Super-Resolution Land Cover Mapping Using Support Vector Regression

Super-resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the spatial dependence principle used in existing SRM models does not fully reflect the real-world situations, making the resultant fine resolution land cover map often have uncertainty. In this paper, an example-based SRM model using support vector regression (SVR_SRM) was proposed. Without directly using an explicit formulation to describe the prior information about the subpixel spatial pattern, SVR_SRM generates a fine resolution land cover map from coarse fractional images, by learning the nonlinear relationships between the coarse fractional pixels and corresponding labeled subpixels from the selected best-match training data. Based on the experiments of two subset images of National Land Cover Database (NLCD) 2001 and a subset of real hyperspectral Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image, the performance of SVR_SRM was evaluated by comparing with the traditional pixel-based hard classification (HC) and several existing typical SRM algorithms. The results show that SVR_SRM can generate fine resolution land cover maps with more detailed spatial information and higher accuracy at different spatial scales.

Interpolation-based super-resolution land cover mapping

Super-resolution mapping (SRM) is a technique to produce a land cover map with finer spatial resolution by using fractional proportion images as input. A two-step SRM approach has been widely used. First, a fine-resolution indicator map is estimated for each class from the coarse-resolution fractional image. All indicator maps are then combined to create the final fine-resolution land cover map. In this letter, three popular interpolation methods, Inverse Distance Weighted (IDW), Spline and Kriging, as well as four indicator map combination strategies, including the maximal value strategy and the sequential assignment strategy with and without normalization, were assessed. Based on the application to two simulated images, the performance of all SRM algorithms was assessed. The results show that the two-step SRM approach can obtain smoother land cover maps than hard classification. An increase in zoom factor results in the appearance of numerous small patches and linear artefacts in the SRM results. The accuracies of Spline and Kriging are similar and are both higher than that of IDW. The maximal value strategy can generate a smoother land cover map than the sequential assignment strategy in most cases, and a normalizing indicator value has a mixed effect on the result.

A NOVEL APPROACH TO SUPER RESOLUTION MAPPING OF MULTISPECTRAL IMAGERY BASED ON PIXEL SWAPPING TECHNIQUE

Abstract. Mixed pixels could be considered as a major source of uncertainty through classification process of satellite imagery. In this regard, the use of soft classifiers is often inevitable in order to increase the accuracy of land cover estimates. Although soft classifiers provide detailed information for each pixel, spatial arrangement of sub-pixels remains unknown. Super Resolution Mapping (SRM) has opened up a new horizon to produce finer spatial resolution maps using the outputs of soft classifiers. Wide variety of SRM algorithms has been developed. In this way, spatial optimisation techniques are the most applicable ones. However, random allocation of sub-pixels and iterative procedure of optimisation are the main limitations of current methods (e.g. Hopfield Neural Network, Simulated Annealing). This research attempts to provide an optimisation approach based on the pixel swapping technique in order to simplify the concept and to reduce the iteration procedure. In this paper, a brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed non-randomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.

Enhanced Self-Training Superresolution Mapping Technique for Hyperspectral Imagery

An efficient superresolution technique through spatial-spectral data fusion for hyperspectral (HS) imagery is proposed in this letter. The spatial and spectral contents of an HS image are extracted using a linear mixture model and a fully constrained least squares unmixing technique. These data are then combined using a spatial correlation model through a learning-based superresolution mapping (SRM) algorithm. The proposed spatial correlation model realistically simulates a mapping model between the low-resolution (LR) HS image and its subsampled version ( LR2 HS image) to train the designed SRM algorithm for mapping from the LR to high resolution. The experiments on real HS images validate the accuracy and low complexity of the proposed autonomous technique for key information detection in HS imagery.