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Showing results 7 to 9 of 9
  • BB

  • 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...
  • BB

  • 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...