Extraction of low cost houses from a high spatial resolution satellite imagery using Canny edge detection filter

Since its democratic dispensation in 1994, the South African government enacted a number of legislative and policy interventions aimed at availing equal housing opportunities to the previously marginalized citizens. Mismanagement and unreliable reporting has been widely reported in publicly funded housing programmes which necessitated the government to audit and monitor housing development projects in municipalities using more robust and independent methodologies. The objective of this study was therefore to test and demonstrate the effectiveness of high spatial resolution satellite imagery in validating the presence of government funded houses using an object-oriented classification technique that applies a Canny edge detection filter. The results of this study demonstrate that object-orientated classification applied on pan-sharpened SPOT 6 satellite imagery can be used to conduct a reliable inventory and validate the number of houses. The application of the multi-resolution segmentation and Canny edge detection filtering technique proved to be an effective means of mapping individual houses as shown by the high detection accuracy of 99% and quality percentage of 96%.Keywords: Houses, Remote Sensing, SPOT 6, Canny edge detection, Multi-resolution Segmentation, Object-Oriented Classificatio

Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models

This research is concerned with a methodology for automated generation of polyhedral building models for complex structures, whose rooftops are bounded by straight lines. The process starts by utilizing LiDAR data for building hypothesis generation and derivation of individual planar patches constituting building rooftops. Initial boundaries of these patches are then refined through the integration of LiDAR and photogrammetric data and hierarchical processing of the planar patches. Building models for complex structures are finally produced using the refined boundaries. The performance of the developed methodology is evaluated through qualitative and quantitative analysis of the generated building models from real data

BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method

Monoscopic method for the automatic determination of height of buildings in air photos

This paper proposes a monoscopic method for automatic determination ofbuilding’s heights in digital photographs areas, based on radial displacement ofpoints in the plan image and geometry at the time the photo is obtained.Determination of the buildings’ heights can be used to model the surface in urbanareas, urban planning and management, among others. The proposed methodologyemploys a set of steps to detect arranged radially from the system ofphotogrammetric coordinates, which characterizes the lateral edges of buildingspresent in the photo. In a first stage is performed the reduction of the searching areathrough detection of shadows projected by buildings, generating sub-images of theareas around each of the detected shadow. Then, for each sub-image, the edges areautomatically extracted, and tests of consistency are applied for it in order to becharacterized as segments of straight arranged radially. Next, with the lateral edgesselected and the knowledge of the flight height, the buildings’ heights can becalculated. The experimental results obtained with real images showed that theproposed approach is suitable to perform the automatic identification of thebuildings height in digital images.Este trabalho propõe um método monoscópico para a determinação automática daaltura de edifícios em fotografias aéreas digitais, baseada no deslocamento radialdos pontos projetados no plano-imagem e na geometria no momento da obtenção dafotografia. A determinação da altura de edifícios pode ser utilizada para amodelagem da superfície em áreas urbanas, planejamento e gerenciamento urbano,entre outros. A metodologia proposta emprega um conjunto de etapas para adetecção de bordas dispostas radialmente em relação ao sistema de coordenadasfotogramétrico, sendo que estas bordas caracterizam as arestas laterais verticais dosedifícios presentes numa fotografia. Na primeira etapa realiza-se a redução doespaço de busca por meio da detecção das sombras projetadas pelos edifícios,gerando sub-imagens das áreas no entorno de cada sombra detectada. Em seguida,para cada sub-imagem são extraídas automaticamente bordas que passam por testesde consistência de modo que sejam caracterizados como segmentos de retadispostos radialmente. Deste modo, a partir destas arestas, e com o conhecimento daaltura de vôo, são estimadas as alturas dos edifícios. Os resultados alcançados emexperimentos com imagens reais digitalizadas, obtidas com câmaras métricas,mostraram-se promissores para a determinação das alturas de edifícios

Evaluation of automatic building detection approaches combining high resolution images and LiDAR data

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. © 2011 by the authors.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and the support of the Spanish Instituto Geografico Nacional (IGN).Hermosilla, T.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Estornell Cremades, J. (2011). Evaluation of automatic building detection approaches combining high resolution images and LiDAR data. Remote Sensing. 3:1188-1210. https://doi.org/10.3390/rs3061188S118812103Mayer, H. (1999). 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S., Shan, J., & Bethel, J. S. (2003). Class-Guided Building Extraction from Ikonos Imagery. Photogrammetric Engineering & Remote Sensing, 69(2), 143-150. doi:10.14358/pers.69.2.143STASSOPOULOU, A., & CAELLI, T. (2000). BUILDING DETECTION USING BAYESIAN NETWORKS. International Journal of Pattern Recognition and Artificial Intelligence, 14(06), 715-733. doi:10.1142/s0218001400000477Jin, X., & Davis, C. H. (2005). Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information. EURASIP Journal on Advances in Signal Processing, 2005(14). doi:10.1155/asp.2005.2196Kim, Z., & Nevatia, R. (1999). Uncertain Reasoning and Learning for Feature Grouping. Computer Vision and Image Understanding, 76(3), 278-288. doi:10.1006/cviu.1999.0803Dare, P. M. (2005). Shadow Analysis in High-Resolution Satellite Imagery of Urban Areas. 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IEEE Transactions on Geoscience and Remote Sensing, 44(9), 2523-2533. doi:10.1109/tgrs.2006.874137Lafarge, F., Descombes, X., Zerubia, J., & Pierrot-Deseilligny, M. (2008). Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling. ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), 365-381. doi:10.1016/j.isprsjprs.2007.09.003Yu, B., Liu, H., Wu, J., Hu, Y., & Zhang, L. (2010). Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landscape and Urban Planning, 98(3-4), 210-219. doi:10.1016/j.landurbplan.2010.08.004Paparoditis, N., Cord, M., Jordan, M., & Cocquerez, J.-P. (1998). Building Detection and Reconstruction from Mid- and High-Resolution Aerial Imagery. Computer Vision and Image Understanding, 72(2), 122-142. doi:10.1006/cviu.1998.0722Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Hermosilla, T. (2011). Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. International Journal of Digital Earth, 4(6), 521-538. doi:10.1080/17538947.2010.533201Ruiz, L. A., Recio, J. A., Fernández-Sarría, A., & Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture, 76(2), 284-296. doi:10.1016/j.compag.2011.02.007Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. doi:10.1109/tsmc.1973.4309314Sutton, R. N., & Hall, E. L. (1972). Texture Measures for Automatic Classification of Pulmonary Disease. IEEE Transactions on Computers, C-21(7), 667-676. doi:10.1109/t-c.1972.223572Freund, Y. (1995). Boosting a Weak Learning Algorithm by Majority. Information and Computation, 121(2), 256-285. doi:10.1006/inco.1995.1136Shufelt, J. A. (1999). 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Semi-automated approach for mapping urban trees from integrated airborne based digital image and lidar point cloud datasets

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees feature include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LiDAR and multispectral digital image datasets over Istanbul city of Turkey. The presented approach includes extraction of shadow free vegetation areas from digital images using shadow index and NDVI techniques, automated extraction of 3D information about vegetation areas from integrated processing of the datasets, extraction of tree objects from the vegetation based on various LiDAR attributes and finally, accuracy assessment of the extracted trees. The quality measures of this approach reveals that the extracted result is 83% complete and 80% correct. The developed algorithms have shown a promising result which proved that the integrated datasets is a suitable technology and viable source of information for urban trees management. Furthermore, the approach has also proved to be an accurate, fast and cost effective technique for estimating and delineating 3D information about trees. As a conclusion, therefore, the extracted information provides a snapshot of location, and extent of trees in the study area which will be useful to city planners and decision makers to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions

Building Detection from Very High Resolution Remotely Sensed Imagery Using Deep Neural Networks

The past decades have witnessed a significant change in human societies with a fast pace and rapid urbanization. The boom of urbanization is contributed by the influx of people to the urban area and comes with building construction and deconstruction. The estimation of both residential and industrial buildings is important to reveal and demonstrate the human activities of the regions. As a result, it is essential to effectively and accurately detect the buildings in urban areas for urban planning and population monitoring. The automatic building detection method in remote sensing has always been a challenging task, because small targets cannot be identified in images with low resolution, as well as the complexity in the various scales, structure, and colours of urban buildings. However, the development of techniques improves the performance of the building detection task, by taking advantage of the accessibility of very high-resolution (VHR) remotely sensed images and the innovation of object detection methods. The purpose of this study is to develop a framework for the automatic detection of urban buildings from the VHR remotely sensed imagery at a large scale by using the state-of-art deep learning network. The thesis addresses the research gaps and difficulties as well as the achievements in building detection. The conventional hand-crafted methods, machine learning methods, and deep learning methods are reviewed and discussed. The proposed method employs a deep convolutional neural network (CNN) for building detection. Two input datasets with different spatial resolutions were used to train and validate the CNN model, and a testing dataset was used to evaluate the performance of the proposed building detection method. The experiment result indicates that the proposed method performs well at both building detection and outline segmentation task with a total precision of 0.92, a recall of 0.866, an F1-score of 0.891. In conclusion, this study proves the feasibility of CNN on solving building detection challenges using VHR remotely sensed imagery