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      • Department of Computer Engineering
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      Weakly supervised instance attention for multisource fine-grained object recognition with an application to tree species classification

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      Embargo Lift Date: 2023-06-30
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      Author(s)
      Aygüneş, Bulut
      Cinbiş, R. G.
      Aksoy, Selim
      Date
      2021-06
      Source Title
      ISPRS Journal of Photogrammetry and Remote Sensing
      Print ISSN
      0924-2716
      Publisher
      Elsevier BV
      Volume
      176
      Pages
      262 - 274
      Language
      English
      Type
      Article
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      Abstract
      Multisource image analysis that leverages complementary spectral, spatial, and structural information benefits fine-grained object recognition that aims to classify an object into one of many similar subcategories. However, for multisource tasks that involve relatively small objects, even the smallest registration errors can introduce high uncertainty in the classification process. We approach this problem from a weakly supervised learning perspective in which the input images correspond to larger neighborhoods around the expected object locations where an object with a given class label is present in the neighborhood without any knowledge of its exact location. The proposed method uses a single-source deep instance attention model with parallel branches for joint localization and classification of objects, and extends this model into a multisource setting where a refer- ence source that is assumed to have no location uncertainty is used to aid the fusion of multiple sources in four different levels: probability level, logit level, feature level, and pixel level. We show that all levels of fusion provide higher accuracies compared to the state-of-the-art, with the best performing method of feature-level fusion resulting in 53% accuracy for the recognition of 40 different types of trees, corresponding to an improvement of 5.7% over the best performing baseline when RGB, multispectral, and LiDAR data are used. We also provide an in-depth comparison by evaluating each model at various parameter complexity settings, where the increased model capacity results in a further improvement of 6.3% over the default capacity setting.
      Keywords
      Multisource classification
      Fine-grained object recognition
      Weakly supervised learning
      Deep learning
      Permalink
      http://hdl.handle.net/11693/77451
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.isprsjprs.2021.03.021
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