Optimizing hopfield neural network for super-resolution mapping

Remote sensing is a potential source of information of land covers on the surface of the Earth. Different types of remote sensing images offer different spatial resolution quality. High resolution images contain rich information, but they are expensive, while low resolution image are less detail but they are cheap. Super-resolution mapping (SRM) technique is used to enhance the spatial resolution of the low resolution image in order to produce land cover mapping with high accuracy. The mapping technique is crucial to differentiate land cover classes. Hopfield neural network (HNN) is a popular approach in SRM. Currently, numerical implementation of HNN uses ordinary differential equation (ODE) calculated with traditional Euler method. Although producing satisfactory accuracy, Euler method is considered slow especially when dealing with large data like remote sensing image. Therefore, in this paper several advanced numerical methods are applied to the formulation of the ODE in SRM in order to speed up the iterative procedure of SRM. These methods are an improved Euler, Runge-Kutta, and Adams-Moulton. Four classes of land covers such as vegetation, water bodies, roads, and buildings are used in this work. Results of traditional Euler produces mapping accuracy of 85.18% computed in 1000 iterations within 220-1020 seconds. Improved Euler method produces accuracy of 86.63% computed in a range of 60-620 iterations within 20-500 seconds. Runge-Kutta method produces accuracy of 86.63% computed in a range of 70-600 iterations within 20-400 seconds. Adams-Moulton method produces accuracy of 86.64% in a range of 40-320 iterations within 10-150 seconds

Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m

An iterative interpolation deconvolution algorithm for superresolution land cover mapping

Super-resolution 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 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 de-convolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation de-convolution (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 de-convolution. The fine spatial resolution land cover map is then updated by re-convolution, back-projection and de-convolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multi-spectral 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

Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

Learning-based superresolution land cover mapping

Super-resolution 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 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 these SRM algorithms, by using the entire maps in the accuracy assessment

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity