| 1. |
- Ban, Yifang, et al.
(författare)
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Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Utilizing a Robust Urban Extractor
- 2015
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : Elsevier BV. - 0924-2716 .- 1872-8235. ; 103
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Tidskriftsartikel (refereegranskat)abstract
- With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrated robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.
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| 2. |
- Jacob, Alexander, 1983-, et al.
(författare)
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Fusion of Multitemporal Multi-Angle ENVISAT ASAR and HJ-1 Data for Object-based Urban Land Cover Classification
- 2012
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Ingår i: FUSION OF MULTITEMPORAL ENVISAT ASAR AND HJ-1 DATA FOR OBJECT-BASED LAND COVER MAPPING. ; , s. 52-57
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The key goal of this work is to analyze the synergistic effects of multitemporal data fusion for urban land cover mapping. In particular this analysis is carried out using multitemporal ENVISAT ASAR images and one Chinese HJ-1 optical image acquired over Beijing in 2009. The major land cover classes are high-density built-up areas, low-density built-up areas, roads, airports, forests, parks, golf courses, grass/pasture, crops, bare fields and water. The methodology used in this research including orthorectification, SAR speckle filtering, and object-based classification. The segmentation is based on the newly developed algorithm KTH-SEG that utilizes an edge-aware region growing and merging approach. Fusion of the various combinations of multitemporal multi-angle SAR data and HJ-1 data were compared with SAR and optical data alone. The preliminary results show that the fusion of ENVISAT ASAR and HJ-1 data performed much better than optical data alone or SAR data alone. The fusion of 4-date SAR data and optical data can achieve similar classification accuracy as the fusion of 8-date SAR data and optical data if multi-angle, dual look direction SAR data with suitable temporal compositions are selected. Compared to eCognition, the KTH-SEG performed better in extracting linear features such as roads and rivers.
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| 3. |
- Jacob, Alexander, 1983-
(författare)
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Multitemporal Remote Sensing for Urban Mapping using KTH-SEG and KTH-Pavia Urban Extractor
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The objective of this licentiate thesis is to develop novel algorithms and improve existing methods for urban land cover mapping and urban extent extraction using multi-temporal remote sensing imagery. Past studies have demonstrated that synthetic aperture radar (SAR) have very good properties for the analysis of urban areas, the synergy of SAR and optical data is advantageous for various applications. The specific objectives of this research are:1. To develop a novel edge-aware region-growing and -merging algorithm, KTH-SEG, for effective segmentation of SAR and optical data for urban land cover mapping;2. To evaluate the synergistic effects of multi-temporal ENVISAT ASAR and HJ-1B multi-spectral data for urban land cover mapping;3. To improve the robustness of an existing method for urban extent extraction by adding effective pre- and post-processing.ENVISAT ASAR data and the Chinese HJ-1B multispectral , as well as TerraSAR-X data were used in this research. For objectives 1 and 2 two main study areas were chosen, Beijing and Shanghai, China. For both sites a number of multitemporal ENVISAT ASAR (30m C-band) scenes with varying image characteristics were selected during the vegetated season of 2009. For Shanghai TerraSAR-X strip-map images at 3m resolution X-band) were acquired for a similar period in 2010 to also evaluate high resolution X-band SAR for urban land cover mapping. Ten major landcover classes were extracted including high density built-up, low density built-up, bare field, low vegetation, forest, golf course, grass, water, airport runway and major road.For Objective 3, eleven globally distributed study areas where chosen, Berlin, Beijing, Jakarta, Lagos, Lombardia (northern Italy), Mexico City, Mumbai, New York City, Rio de Janeiro, Stockholm and Sydney. For all cities ENVISAT ASAR imagery was acquired and for cities in or close to mountains even SRTM digital elevation data.The methodology of this thesis includes two major components, KTH-SEG and KTH-Pavia Urban Extractor. KTH-SEG is an edge aware region-growing and -merging algorithm that utilizes both the benefit of finding local high frequency changes as well as determining robustly homogeneous areas of a low frequency in local change. The post-segmentation classification is performed using support vector machines. KTH-SEG was evaluated using multitemporal, multi-angle, dual-polarization ASAR data and multispectral HJ-1B data as well as TerraSAR-X data. The KTH-Pavia urban extractor is a processing chain. It includes: Geometrical corrections, contrast enhancement, builtup area extraction using spatial stastistics and GLCM texture features, logical operator based fusion and DEM based mountain masking.For urban land cover classification using multitemporal ENVISAT ASAR data, the results showed that KTH-SEG achieved an overall accuracy of almost 80% (0.77 Kappa ) for the 10 urban land cover classes both Beijign and Shanghai, compared to eCognition results of 75% (0.71 Kappa) In particular the detection of small linear features with respect to the image resolution such as roads in 30m resolved data went well with 83% user accuracy from KTH-SEG versus 57% user accuracy using the segments derived from eCognition. The other urban classes which in particular in SAR imagery are characterized by a high degree of heterogeneity were classified superiorly by KTH-SEG. ECognition in general performed better on vegetation classes such as grass, low vegetation and forest which are usually more homogeneous.It is was also found that the combination of ASAR and HJ-1B optical data was beneficial, increasing the final classification accuracy by at least 10% compared to ASAR or HJ-1B data alone. The results also further confirmed that a higher diversity of SAR type images is more important for the urban classification outcome. However, this is not the case when classifying high resolution TerraSAR-X strip-map imagery. Here the different image characteristics of different look angles, and orbit orientation created more confusion mainly due to the different layover and foreshortening effects on larger buildings. The TerraSAR-X results showed also that accurate urban classification can be achieved using high resolution SAR data alone with almost 84% for eight classes around the Shanghai international Airport (high and low density built-up were not separated as well as roads and runways).For urban extent extraction, the results demonstrated that built-up areas can be effectively extracted using a single ENVISAT ASAR image in 10 global cities reaching overall accuracies around 85%, compared to 75% of MODIS urban class and 73% GlobCover Urban class. Multitemporal ASAR can improve the urban extraction results by 5-10% in Beijing. Mountain masking applied in Mumbai and Rio de Janeiro increased the accuracy by 3-5%.The research performed in this thesis has contributed to the remote sensing community by providing algorithms and methods for both extracting urban areas and identifying urban land cover in a more detailed fashion.
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| 4. |
- Jacob, Alexander, 1983-, et al.
(författare)
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Urban land cover mapping with TerraSAR-X using an edge-aware region-growing and merging algorithm
- 2014
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Ingår i: 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). - : IEEE. - 9781479957750 ; , s. 4836-4839
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Konferensbidrag (refereegranskat)abstract
- TerraSAR X data has been analyzed for its suitability of urban land cover mapping using our recently developed object based image analysis tool KTH-SEG, which is based on an edge aware region growing and merging algorithm and a support vector machine classifier. Classification results over the Shanghai International Airport area using 8 classes, Water, Grass, Roads, Buildings, Crops, Forest, Bare Crops and Green Houses have proven with an overall accuracy just shy of 84% that this is very well the case. It has further been investigated which segment sizes and image configuration yield the best results.
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| 5. |
- Lin, Xing, 1980-
(författare)
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Modern GIR Systems : Framework, Retrieval Model and Indexing Techniques
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Geographic information is one of the most important and the most common types of information in human society. It is estimated that more than 70% of all information in the world has some kind of geographic features. In the era of information explosion, information retrieval (IR) tools, such as search engines, are the main tools people used to quickly find the information they need nowadays. Because of the importance of geographic information, recent efforts have been made either by expanding the traditional IR to support a spatial query, or building a GIR in a brand new architecture from the ground such as the SPIRIT project. To some degree, these existing GIR systems could solve users’ information search need with a spatial filter, especially when the users are looking for information on something within a relatively large extent.Despite its advantage on processing geographical information and queries over conventional IR systems, modern GIR systems are also facing challenges including a proper representation and extraction of geographical information within documents, a better information retrieval model for both thematic and geographical information, a fast indexing mechanism for rapid search within documents by thematic and geographical hints, and even a new architecture of system.The objective of this licentiate research is to provide solutions to some of these problems in order to build a better modern GIR system in the future. The following aspects have been investigated in the thesis: a generic conceptual framework and related key technologies for a modern GIR system, a new information retrieval model and algorithm for measuring the relevance scores between documents and queries in GIR, and finally a new better indexing technique to geographically and thematically index the documents for a faster query processing within modern GIR.Concerning the proposed conceptual framework for modern GIR, it includes three modules: (1) the user interface module, (2) the information extractor, storage and indexer module and (3) the query processing and information retrieval module. Two knowledge bases, Gazetteer and Thesaurus, play an important role in the proposed framework. A digital map based user interface is proposed for the input of user information search needs and representation of retrieval results. Key techniques required for the implementation of a modern GIR using the proposed framework are a proper representation of document and query information, a better geographical information extractor, an innovative information retrieval model and relevance ranking algorithm, and a combined indexing mechanism for both geographical and thematic information.The new information retrieval model is established based on a Spatial Bayesian Network consisting of place names appeared in a single document and the spatial relationships between them. The new model assesses the geographical relevance between GIR document and query by the geographical importance and adjacency of the document geo-footprint versus the geographical scope of the user’s query.Regarding the indexing mechanism for modern GIR systems, a Keyword-Spatial Hybrid Index (KSHI) is proposed for the single and overall geo-footprint model, in which there is only one single geo-footprint for each document to retrieve from. A Keyword-Spatial Dual Index (KSDI) is proved to be more appropriate for a GIR system which allows for multiple geo-footprints within a single document.In addition to theoretical analysis, necessary experiments have also been carried out to evaluate the efficiency of proposed new information retrieval model and indices. Both the theoretical analysis and results of experiments show the potentials of proposed solution and techniques.
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| 6. |
- Mao, Bo, 1983-, et al.
(författare)
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A multiple representation data structure for dynamic visualisation of generalised 3D city models
- 2011
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Ingår i: ISPRS Journal of Photogrammetry and Remote Sensing. - : Elsevier BV. - 0924-2716 .- 1872-8235. ; 66:2, s. 198-208
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a novel multiple representation data structure for dynamic visualisation of 3D city models, called CityTree, is proposed. To create a CityTree, the ground plans of the buildings are generated and simplified. Then, the buildings are divided into clusters by the road network and one CityTree is created for each cluster. The leaf nodes of the CityTree represent the original 3D objects of each building, and the intermediate nodes represent groups of close buildings. By utilising CityTree, it is possible to have dynamic zoom functionality in real time. The CityTree methodology is implemented in a framework where the original city model is stored in CityGML and the CityTree is stored as X3D scenes. A case study confirms the applicability of the CityTree for dynamic visualisation of 3D city models. (C) 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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| 7. |
- Mao, Bo, 1983-, et al.
(författare)
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Detection and typification of linear structures for dynamic visualization of 3D city models
- 2012
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Ingår i: Computers, Environment and Urban Systems. - : Elsevier BV. - 0198-9715 .- 1873-7587. ; 36:3, s. 233-244
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Tidskriftsartikel (refereegranskat)abstract
- Cluttering is a fundamental problem in 3D city model visualization. In this paper, a novel method for removing cluttering by typification of linear building groups is proposed. This method works. in static as well as dynamic visualization of 3D city models. The method starts by converting building models in higher Levels of Details (LoDs) into LoD1 with ground plan and height. Then the Minimum Spanning Tree (MST) is generated according to the distance between the building ground plans. Based on the MST, linear building groups are detected for typification. The typification level of a building group is determined by its distance to the viewpoint as well as its viewing angle. Next, the selected buildings are removed and the remaining ones are adjusted in each group separately. To preserve the building features and their spatial distribution, Attributed Relational Graph (ARC) and Nested Earth Mover's Distance (NEMD) are used to evaluate the difference between the original building objects and the generalized ones. The experimental results indicate that our method can reduce the number of buildings while preserving the visual similarity of the urban areas. (C) 2011 Elsevier Ltd. All rights reserved.
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| 8. |
- Mao, Bo, 1983-, et al.
(författare)
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Generalisation of textured 3D city models using image compression and multiple representation data structure
- 2013
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : Elsevier BV. - 0924-2716 .- 1872-8235. ; 79, s. 68-79
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Tidskriftsartikel (refereegranskat)abstract
- Texture is an essential part of 3D building models and it often takes up a big proportion of the data volume, thus makes dynamic visualization difficult. To compress the texture of 3D building models for the dynamic visualization in different scales, a multi-resolution texture generalization method is proposed, which contains two steps: texture image compression and texture coloring. In the first step, the texture images are compressed in both horizontal and vertical directions using wavelet transform. In the second step, TextureTreeis created to store the building color texture for the dynamic visualization from different distances. To generate TextureTree, texture images are iteratively ｓegmented by horizontal and vertical dividing zone, e.g. edge or background from edge detection, until each section is basically in the same color. Thentexture in each section is represented by their main color and the TextureTree iscreated based on the color difference between the adjacent sections. In dynamic visualization, the suitable compressed texture images or the TextureTree nodes are selected to generate the 3D scenes based on the angle and the distance between user viewpoint and the building surface. The experimental results indicate that the wavelet based image compression and proposed TextureTree can effectively represent the visual features of the textured buildings with much less data.
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| 9. |
- Mao, Bo, 1983-, et al.
(författare)
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Online Visualisation of a 3D City Model Using CityGML and X3DOM
- 2011
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Ingår i: Cartographica. - : University of Toronto Press Inc. (UTPress). - 0317-7173 .- 1911-9925. ; 46:2, s. 109-114
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a novel framework for online visualization of 3D city models. CityGML is used to represent the city models, based on which 3D scenes in X3D are generated, then dynamically updated to the user side with AJAX and visualized in WebGL-supported browsers with X3DOM. The experimental results show that the proposed framework can easily be implemented using widely supported major browsers and can efficiently support online visualization of 3D city models in small areas. For the visualization of large volumes of data, generalization methods and multiple-representation data structure should be studied in future research.
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| 10. |
- Mao, Bo, 1983-, et al.
(författare)
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Real time visualisation of 3D city models in street view based on visual salience
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Ingår i: International Journal of Geographical Information Science. - 1365-8816 .- 1365-8824.
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Tidskriftsartikel (refereegranskat)abstract
- Street level visualization is an important application of the 3D city models. Challenges in the street level visualization are the cluttering of the detailed buildings and the performance. In this paper, a novel method for street level visualization based on visual salience evaluation is proposed. The basic idea of the method is to preserve these salient buildings in a view and remove the non-salient ones. The method is composed by pre-process and real-timevisualization. The pre-process starts by converting 3D building models in higher Levels of Detail (LoDs) into LoD1 with simplified ground plan. Then a number of index view points are created along the streets; these indexes refer both to the positions and the direction of the sights. A visual salience value is computed for each visible simplified building in respective index. The salience of the visible building is calculated based on the visual difference of the original and generalized models. We propose and evaluate three methods for visual salience: local difference, global difference and minimum projection area. The real-time visualization process starts by mapping the observer to its closest indexes. Then the street view is generated based on the building information stored in theindexes. A user study shows that the local visual salience gives better result than the global and area, and the proposed method can reduce the number of loaded building by 90% while still preserve the visual similarity with the original models.
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