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      Automatic detection of geospatial objects using multiple hierarchical segmentations

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      Author(s)
      Akçay, H. G.
      Aksoy, S.
      Date
      2008-07
      Source Title
      IEEE Transactions on Geoscience and Remote Sensing
      Print ISSN
      0196-2892
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      46
      Issue
      7
      Pages
      2097 - 2111
      Language
      English
      Type
      Article
      Item Usage Stats
      270
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      343
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      Abstract
      The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes. © 2008 IEEE.
      Keywords
      Hierarchical segmentation
      Image segmentation
      Object-based analysis
      Unsupervised object detection
      Boolean functions
      Feature extraction
      Image processing
      Information theory
      Learning algorithms
      Mathematical morphology
      Probability
      Probability distributions
      Risk assessment
      Set theory
      Applied (CO)
      Automatic detection
      Automatic labelling
      Comparative experiments
      Conditional probability distributions
      Connected components
      Data sets
      Different scales
      Feature distribution
      Generic algorithms
      Geo spatial objects
      Grouping problems
      High-resolution (HR) images
      Individual (PSS 544-7)
      Kullback leibler divergence (KLD)
      Morphological operations
      New algorithm
      Novel methods
      Object classes
      Object detection
      Object detection algorithms
      Object modelling
      Object-based
      Probabilistic latent semantic analysis (PLSA)
      Remotely sensed imagery (RSI)
      Segmentation algorithms
      Spectral banding
      Spectral informations
      Structural informations
      Structuring element (SE)
      Unsupervised segmentation
      Object recognition
      Algorithms
      Image analysis
      Information retrieval
      Labels
      Mathematical models
      Remote sensing
      Permalink
      http://hdl.handle.net/11693/23067
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TGRS.2008.916644
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      • Department of Computer Engineering 1510
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