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  • 1
    Language: English
    In: IEEE journal of selected topics in applied earth observations and remote sensing, 2014-06, Vol.7 (6), p.2405-2418
    Description: The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.
    Subject(s): VHR imagery ; Laser radar ; Light Detection And Ranging (LiDAR) ; urban ; Data integration ; Vegetation mapping ; hyperspectral ; Feature extraction ; Data fusion ; multi-modal ; Hyperspectral imaging
    ISSN: 1939-1404
    E-ISSN: 2151-1535
    Source: IEEE Electronic Library (IEL)
    Source: Directory of Open Access Journals
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-03, Vol.54 (3), p.1738-1756
    Description: Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of "leakage" (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.
    Subject(s): partial reconstruction ; high spatial resolution ; hyperspectral data ; Shape ; Morphology ; Attribute profiles (APs) ; Feature extraction ; classification ; Image reconstruction ; Hyperspectral imaging ; Research ; Image processing ; Remote sensing ; Geographic information systems ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Language: English
    In: IEEE journal of selected topics in applied earth observations and remote sensing, 2012-08, Vol.5 (4), p.1177-1190
    Description: When using morphological features for the classification of high resolution hyperspectral images from urban areas, one should consider two important issues. The first one is that classical morphological openings and closings degrade the object boundaries and deform the object shapes. Morphological openings and closings by reconstruction can avoid this problem, but this process leads to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction. The second one is that the morphological profiles (MPs) with different structuring elements and a range of increasing sizes of morphological operators produce high-dimensional data. These high-dimensional data may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. In this paper, we first investigate morphological profiles with partial reconstruction and directional MPs for the classification of high resolution hyperspectral images from urban areas. Secondly, we develop a semi-supervised feature extraction to reduce the dimensionality of the generated morphological profiles for the classification. Experimental results on real urban hyperspectral images demonstrate the efficiency of the considered techniques.
    Subject(s): high spatial resolution ; hyperspectral data ; Shape ; Urban areas ; semi-supervised feature extraction ; Classification ; Feature extraction ; Image reconstruction ; Hyperspectral imaging ; morphological profiles
    ISSN: 1939-1404
    E-ISSN: 2151-1535
    Source: Directory of Open Access Journals
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Language: English
    In: Sustainability (Basel, Switzerland), 2016-11-07, Vol.8 (11), p.1142
    Description: It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations.
    Subject(s): Policy 2.0 ; transport planning ; urban data analytics ; decision making ; sustainable mobility ; smartphones ; big data ; smart city
    ISSN: 2071-1050
    E-ISSN: 2071-1050
    Source: Directory of Open Access Journals
    Source: ProQuest Central
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Language: English
    In: IEEE geoscience and remote sensing letters, 2015-03, Vol.12 (3), p.552-556
    Description: Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.
    Subject(s): hyperspectral image (HSI) ; Laser radar ; Accuracy ; Urban areas ; Feature extraction ; Data fusion ; remote sensing ; graph-based ; light detection and ranging (LiDAR) data ; Hyperspectral imaging
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2008-10, Vol.46 (10), p.2803-2813
    Description: Meter to submeter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. One possible approach is the use of morphological profiles (MPs). In this paper, we introduce two improvements on the use of MPs. Current approaches use disk-shaped structuring elements (SEs) to derive an MP. This profile contains information about the minimum dimension of objects. In this paper, we extend this approach by using linear SEs. This results in a profile containing information about the maximum object dimension. We show that the addition of the line-based MP gives a substantial improvement of the classification result. A second improvement is achieved by using ldquopartial morphological reconstructionrdquo instead of the normal morphological reconstruction. Morphological reconstruction is commonly used to better preserve the shape of objects. However, we show that this leads to ldquoover-reconstructionrdquo in typical remote sensing images and a decreased classification performance. With ldquopartial reconstruction,rdquo we are able to overcome this problem and still preserve the shape of objects.
    Subject(s): Image resolution ; Shape measurement ; Urban areas ; high-resolution imagery ; Data mining ; Image reconstruction ; Image segmentation ; Satellites ; Morphology ; Classification ; mathematical morphology ; Spatial resolution ; Pixel ; Internal geophysics ; Earth, ocean, space ; Applied geophysics ; Earth sciences ; Exact sciences and technology ; Environmental aspects ; Metropolitan areas ; Automatic classification ; Mathematical analysis ; Satellite imaging ; Methods
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Language: English
    In: International journal of ITS research, 2015-01, Vol.13 (1), p.17-27
    Description: The MOVE project deals with the collection and analysis of crowd behaviour data. The main goals of the project are to collect data through the use of mobile phones and to develop new technologies to process and mine the collected data for crowd behaviour analysis. This paper describes the different steps in the development of tracking applications for smartphones that make use of advanced data mining. The results on data collection, analysis, and reporting have led to the development and operation of an advanced urban data monitoring system.
    Subject(s): Travel behaviour ; Engineering ; Urban mobility ; Computer Imaging, Vision, Pattern Recognition and Graphics ; Civil Engineering ; User Interfaces and Human Computer Interaction ; Crowd behaviour data ; Data collection ; Automotive Engineering ; Electrical Engineering ; Robotics and Automation ; Data mining ; Usage ; Smart phones
    ISSN: 1348-8503
    E-ISSN: 1868-8659
    Source: Alma/SFX Local Collection
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    Language: English
    Description: It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations.
    Subject(s): Social Sciences ; urban data analytics ; BEHAVIOR ; Technology and Engineering ; Policy 2.0 ; IMPACT ; transport planning ; decision making ; sustainable mobility ; FRAMEWORK ; smartphones ; big data ; SMART CITIES ; smart city
    ISSN: 2071-1050
    E-ISSN: 2071-1050
    Source: Ghent University Academic Bibliography
    Source: ProQuest Central
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Language: English
    Description: Meter to submeter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. One possible approach is the use of morphological profiles (MPs). In this paper, we introduce two improvements on the use of MPs. Current approaches use disk-shaped structuring elements (SEs) to derive an MP. This profile contains information about the minimum dimension of objects. In this paper, we extend this approach by using linear SEs. This results in a profile containing information about the maximum object dimension. We show that the addition of the line-based NIP gives a substantial improvement of the classification result. A second improvement is achieved by using "partial morphological reconstruction" instead of the normal morphological reconstruction. Morphological reconstruction is commonly used to better preserve the shape of objects. However, we show that this leads to "over-reconstruction" in typical remote sensing images and a decreased classification performance. With "partial reconstruction," we are able to overcome this problem and still preserve the shape of objects.
    Subject(s): Technology and Engineering
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: Ghent University Academic Bibliography
    Source: IEEE Electronic Library (IEL)
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2007-04, Vol.45 (4), p.810-817
    Description: In this paper, we present a graph-based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image features and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows determining the bounds of error tolerance and, in this way, characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy, the derived bounds of error tolerance are essential
    Subject(s): Computer vision ; Geographic Information Systems ; Content based retrieval ; Image retrieval ; Change detection ; graph matching ; Information retrieval ; Spatial databases ; Data mining ; relevance ; geographic information system (GIS) ; Image databases ; Training data ; Error correction ; Information storage and retrieval ; Analysis
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
    Library Location Call Number Volume/Issue/Year Availability
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