Settlement type classification using aerial images.

M. Sc. University of KwaZulu-Natal, Durban 2014.In metropolitan and urban areas, the problems relating to rapid transformations that are taking place in terms of land cover and land use are now very pronounced, e.g., the rapid increase and unpredictable spread of formal and informal physical infrastructure. As a result, the availability of detailed, timely information on urban areas is of considerable importance both to the management of current urban activities and to forward planning. Remote sensing sources can make a vital contribution in this context, since they provide regular and recurring data from a single, consistent source. Pattern recognition techniques have been demonstrated to be effective in distinguishing and classifying human settlements. However, these methods are not ideal as they perform poorly when presented with imagery of the same area acquired at different dates. The poor generalization ability is mainly caused by large off-nadir viewing angles which produce image pairs with different viewing- and illumination-geometries. Classification performance is also decreased by differences in shadow length and orientation. The objective of this research is to improve the generalisation ability of the automated classification of human settlements using only remote sensing data over urban areas. The multiresolution local binary patterns (LBPs) algorithm, extended with an orthogonal variance measure for measuring local contrast features (i.e., the extended LBP) has been shown to excel at texture classification tasks. To minimize the viewing- and illumination-geometry effects and improve settlement classification, the extended LBP was applied to high spatial resolution panchromatic aerial images. The addition of a contrast component to the LBP features does not directly affect the desired invariance to shadow orientation and length, but it is expected that the richer features will nevertheless improve settlement classification accuracy. The extended LBP method was evaluated using a support vector machine (SVM) classifier for cross-date (training and test images of the same area acquired at different dates) and samedate analysis. For comparable results, LBPs without contrast features were also evaluated. The results showed the extended LBP to have a strong spatial and temporal generalisation ability for classifying settlements of aerial images, when compared to its counterpart. From this research, we can conclude that the extended LBP’s additional contrast features can improve overall settlement type classification accuracy and generalisation ability

Improvements of local directional pattern for texture classification.

Doctoral Degree. University of KwaZulu-Natal, Durban.The Local Directional Pattern (LDP) method has established its effectiveness and performance compared to the popular Local Binary Pattern (LBP) method in different applications. In this thesis, several extensions and modification of LDP are proposed with an objective to increase its robustness and discriminative power. Local Directional Pattern (LDP) is dependent on the empirical choice of three for the number of significant bits used to code the responses of the Kirsch Mask operation. In a first study, we applied LDP on informal settlements using various values for the number of significant bits k. It was observed that the change of the value of the number of significant bits led to a change in the performance, depending on the application. Local Directional Pattern (LDP) is based on the computation Kirsch Mask application response values in eight directions. But this method ignores the gray value of the center pixel, which may lead to loss of significant information. Centered Local Directional Pattern (CLDP) is introduced to solve this issue, using the value of the center pixel based on its relations with neighboring pixels. Local Directional Pattern (LDP) also generates a code based on the absolute value of the edge response value; however, the sign of the original value indicates two different trends (positive or negative) of the gradient. To capture the gradient trend, Signed Local Directional Pattern (SLDP) and Centered-SLDP (C-SLDP) are proposed, which compute the eight edge responses based on the two different directions (positive or negative) of the gradients.The Directional Local Binary pattern (DLBP) is introduced, which adopts directional information to represent texture images. This method is more stable than both LDP and LBP because it utilizes the center pixel as a threshold for the edge response of a pixel in eight directions, instead of employing the center pixel as the threshold for pixel intensity of the neighbors, as in the LBP method. Angled Local directional pattern (ALDP) is also presented, with an objective to resolve two problems in the LDP method. These are the value of the number of significant bits k, and to taking into account the center pixel value. It computes the angle values for the edge response of a pixel in eight directions for each angle (0◦,45◦,90◦,135◦). Each angle vector contains three values. The central value in each vector is chosen as a threshold for the other two neighboring pixels. Circular Local Directional Pattern (CILDP) isalso presented, with an objective of a better analysis, especially with textures with a different scale. The method is built around the circular shape to compute the directional edge vector using different radiuses. The performances of LDP, LBP, CLDP, SLDP, C-SLDP, DLBP, ALDP and CILDP are evaluated using five classifiers (K-nearest neighbour algorithm (k-NN), Support Vector Machine (SVM), Perceptron, Naive-Bayes (NB), and Decision Tree (DT)) applied to two different texture datasets: Kylberg dataset and KTH-TIPS2-b dataset. The experimental results demonstrated that the proposed methods outperform both LDP and LBP

A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

A family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not improve the classification accuracy achieved from only the spectral information. Lastly, both approaches (HEP and GLCM) showed similar behavior with regard to the training set size applied

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

The effects of segmentation-based shadow removal on across-date settlement type classification of panchromatic QuickBird images

Settlement classifiers for multitemporal satellite image analysis have to overcome numerous difficulties related to across-date variances in viewing- and illumination geometry. Shadow anisotropy is a prominent contributing factor in classifier inaccuracy, so methods are introduced in this study to enable minimum-supervision classifier design that mitigate the effects of shadow profile differences. A segmentation-based shadow detector is proposed that utilizes a panchromatic segment merging algorithm with parameters that are robust against dynamic range variances seen in multitemporal imagery. The proposed shadow detector improves on the settlement classification accuracy achieved by fixed threshold detection paired with shadow removal in the presented case-study. The relationship between shadow detection accuracy and settlement classification accuracy is investigated, and it is shown that shadow removal produces greater settlement accuracy improvements for across-date experiments specifically.National Research Foundation (NRF)http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=4609443hb2013ai201

Global spatial and temporal analysis of human settlements from Optical Earth Observation: Concepts, procedures, and preliminary results

This report provides an overview on the concepts, processing procedures and examples used to quantify changes in built-up land from optical satellite imagery. This is part of the larger work of the Global Human Settlement (GHS) team from the Joint Research Centre (JRC) that aims to measure the spatial extent of global human settlements, to monitor its changes over time and characterize the morphology of settlements. This built-up change analysis addresses the quantification of urbanization including some socio-economic and physical processes associated with urbanization. This includes the quantification of the building stock for modeling physical exposure in disaster risk modeling, as background layer for emergency response when a disaster unfolds and as background building stock layer for normalizing physical loss data. Based on the application of three of the most used change detection methods, Principal Component Analysis, Image Differencing Comparison, and Post-Classification Comparison, we present and discuss preliminary results, and try to identify future research directions for developing an appropriate approach for GHSL result images. The case studies were carried on Alger and Dublin city areas.JRC.G.2-Global security and crisis managemen

Utility of High Resolution Human Settlement Data for Assessment of Electricity Usage Patterns

Electricity is vital for modern human civilization, and its demands are expected to significantly rise due to urban growth, transportation modernization, and increasing industrialization and energy accessibility. Meeting the present and future demands while minimizing the environmental degradation from electricity generation pathways presents a significant sustainability challenge. Urban areas consume around 75% of global energy supply yet urban energy statistics are scarce all over the world, creating a severe hindrance for the much-needed energy sustainability studies. This work explores the scope of geospatial data-driven analysis and modeling to address this challenge. Identification and measurements of human habitats, a key measure, is severely misconceived. A multi-scale analysis of high, medium, and coarse resolution datasets in Egypt and Taiwan illustrates the increasing discrepancies from global to local scales. Analysis of urban morphology revealed that high-resolution datasets could perform much better at all scales in diverse geographies while the power of other datasets rapidly diminishes from the urban core to peripheries. A functional inventory of urban settlements was developed for three cities in the developing world using very high-resolution images and texture analysis. Analysis of correspondence between nighttime lights emission, a proxy of electricity consumption, and the settlement inventory was the conducted. The results highlight the statistically significant relationship between functional settlement types and corresponding light emission, and underline the potential of remote sensing data-driven methods in urban energy usage assessment. Lastly, the lack of urban electricity data was addressed by a geospatial modeling approach in the United States. The estimated urban electricity consumption was externally validated and subsequently used to quantify the effects of urbanization on electricity consumption. The results indicate a 23% lowering of electricity consumption corresponding to a 100% increase in urban population. The results highlight the potential of urbanization in lowering per-capita energy usage. The opportunity and limits to such energy efficiency were identified with regards to urban population density. The findings from this work validate the applicability of geospatial data in urban energy studies and provide unique insights into the relationship between urbanization and electricity demands. The insights from this work could be useful for other sustainability studies