Object-oriented change detection for the city of Harare, Zimbabwe

Gamanya, Ruvimbo; Maeyer, Philippe De; Dapper, Morgan De

doi:10.1016/j.eswa.2007.09.067

Cited by 95 publications

(60 citation statements)

References 20 publications

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“…The result of this work exhibits the buildings obtained through a totally automatic way. In this we made possible to view the significant progress as well as the limitations in this topic (Gamanya, Maeyer & Dapper, 2009). Final products were confirmed in field and we evidenced the last technologies in image segmentation, mixed with point clouds LIDAR and the integration of the different algorithms of class assignation that allow us to automatically obtain the buildings of the working area with a 97.2% 2 trust.…”

Section: Introductionmentioning

confidence: 93%

Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Barragán¹,

Campos²,

Sanchez³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Cartography, Automatic Extraction, Photogrammetry, Aerial Photographs. Image Segmentation ABSTRACT:The objective of this research is automatic generation of buildings in the interest areas. This research was developed by using high resolution vertical aerial photographs and the LIDAR point cloud through radiometric and geometric digital processes. The research methodology usesknown building heights and various segmentation algorithms and spectral band combination. The overall effectiveness of the algorithm is 97.2% with the test data.

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Section: Introductionmentioning

confidence: 93%

Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Barragán¹,

Campos²,

Sanchez³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Studies using satellite images (Al-Khudhairy et al, 2005;McDermid et al, 2008;Niemeyer et al, 2008;Hofmann et al, 2008;Gamanya et al, 2009;Bouziani et al, 2010;Huang et al, 2014) did not use DSM for CD. CD was based solely on the analysis of image data.…”

Section: Current Problems and Proposed Solutionsmentioning

confidence: 99%

“…This study is based on (Hofmann, 2001) which is dedicated to the same topic, but uses the IKONOS images. The hierarchical image segmentation approach of multitemporal Landsat TM and ETM images was adopted by (Gamanya et al, 2009). The aim of this study is to perform CD by using fuzzy logic classification implemented in Definiens Professional software.…”

Section: Hybrid Change Detection Using Satellite Imagesmentioning

confidence: 99%

Nationwide Hybrid Change Detection of Buildings

Hron

Halounová

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/5 KEY WORDS: Buildings, Change Detection, Pixel-based, Object-based, Aerial Photographs, Open-source ABSTRACT:The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.

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“…producing salt-paper results due to high spectral diversity, high time processing and inability to interpret image due to weakness of pixel information) (Chen, Hay et al 2012). Therefore, with the development of objectbased techniques, the unit of image analysis changed from pixel to object (Blaschke 2010) and the limitations of pixel-based techniques have been solved (Hall and Hay 2003, Laliberte, Rango et al 2004, Blaschke 2005, Desclée, Bogaert et al 2006, Bontemps, Bogaert et al 2008, Gamanya, De Maeyer et al 2009). In object-based techniques, homogeneous image objects have more signal to noise ratio rather than image pixels.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous Ant Colony Algorithm With Emphasis on Building Detection

Tamimi

Ebadi

Kiani

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize objectbased SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

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Object-oriented change detection for the city of Harare, Zimbabwe

Cited by 95 publications

References 20 publications

Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Nationwide Hybrid Change Detection of Buildings

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous Ant Colony Algorithm With Emphasis on Building Detection

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