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Showing results 3 to 9 of 9
  • BB

  • Mangrove classification using support vector machines and random forest algorithm : a comparative study

    • Authors: Campomanes, F.;  Advisor: -;  Participants: - (2016)

  • Mangrove forest ecosystems fulfil a number of important functions like supporting the conservation of biological diversity by providing habitats, nurseries, and nutrients for animal species. In the Philippines, mangrove forests are declining due to the growth of aquaculture production. Mangrove forests are slowly being replaced by fishponds. An accurate inventory of what are left of these natural resources is important to know how we can conserve and manage them. This study aims to compare the performance of support vector machines (SVM) with random forest (RF) algorithm in automatically classifying mangrove forests using LiDAR data and orthophotographs in an object based approach. Th...
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  • Robustness of rule sets using VHR imagery to detect informal settlements : a case of Mumbai, India

    • Authors: Naorem, V.;  Advisor: -;  Participants: - (2016)

  • Robust monitoring approaches for informal settlements using very high-resolution (VHR) satellite imagery can deliver essential information for supporting the formulation of pro-poor policies. Such information can complement census methods or participatory approaches. With the increasing availability of VHR satellite imagery, detection of the informal settlements benefits from the conceptualization of location-specific knowledge in the form of a locally-adapted generic slum ontology (GSO). In this study, we developed the local slum ontology for Mumbai, India, by incorporating local knowledge with image-based proxies. Then, we translated the local ontology into a rule set using Object B...
  • BB

  • Robustness of rule sets using VHR imagery to detect informal settlements : a case of Mumbai, India

    • Authors: Naorem, V. de;  Advisor: -;  Participants: - (2016)

  • Robust monitoring approaches for informal settlements using very high-resolution (VHR) satellite imagery can deliver essential information for supporting the formulation of pro-poor policies. Such information can complement census methods or participatory approaches. With the increasing availability of VHR satellite imagery, detection of the informal settlements benefits from the conceptualization of location-specific knowledge in the form of a locally-adapted generic slum ontology (GSO). In this study, we developed the local slum ontology for Mumbai, India, by incorporating local knowledge with image-based proxies. Then, we translated the local ontology into a rule set using Object B...
  • BB

  • RSOBIA - A new OBIA Toolbar and Toolbox in ArcMap 10.x for Segmentation and Classification

    • Authors: Bas, T.P. le;  Advisor: -;  Participants: - (2016)

  • A need was therefore seen for OBIA to be integrated into GIS. ESRI’s ArcMap is currently used by millions of people daily worldwide and could benefit from access to OBIA. Easy usage and access is essential. A toolbar for push button control was envisaged and RSOBIA (Remote sensing Object Based Image Analysis) was created. The toolbar is easy to use and requires very little training. For the slightly higher level user a toolbox has also been created, so that the RSOBIA functions can be incorporated into other programming models and allows more complex functions to be created.
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  • Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis

    • Authors: Belgiu, M.;  Advisor: -;  Participants: - (2017)

  • Efficient methodologies for mapping croplands are an essential condition for the implementation of sustainable agricultural practices and for monitoring crops periodically. The increasing spatial and temporal resolution of globally available satellite images, such as those provided by Sentinel-2, creates new possibilities for generating accurate datasets on available crop types, in ready-to-use vector data format. Existing solutions dedicated to cropland mapping, based on high resolution remote sensing data, are mainly focused on pixel-based analysis of time series data. This paper evaluates how a time-weighted dynamic time warping (TWDTW) method that uses Sentinel-2 time series perfo...
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  • Using pure and mixed objects in the training of object-based image classifications.

    • Authors: Costa, H.;  Advisor: -;  Participants: - (2016)

  • Training of object-based land cover classifications is often performed with objects generated via image segmentation. The objects are commonly assumed to be thematically pure or excluded from training if a mixture of classes is associated with them. However, excluding mixed objects has several consequences such as reducing the size of the training data sets. In this study, it is hypothesized that mixed objects may be used in the training stage of a classification to increase the accuracy with which land cover may be mapped from remotely sensed data, with outputs evaluated in relation to a conventional analysis using only pure objects in training. WorldView-2 data covering the Universi...