HIERARCHICAL PROBABILISTIC EMBEDDINGS FOR MULTI-VIEW IMAGE CLASSIFICATION

Benjamin Brodie, Subash Khanal, Muhammad Usman Rafique, Connor Greenwell, Nathan Jacobs

Research output: Contribution to conferencePaperpeer-review

Abstract

We address the task of image classification, when the available spectral bands can vary from image to image. We propose a model that learns to represent uncertainty over latent features in a way that is conditioned on the available bands. We expect that images with fewer bands will generally be more difficult to classify and hence have higher uncertainty. We compare two strategies for training such a model, one which uses explicit hierarchical constraints and one which relies on implicit constraints. We evaluate both using RGB and multispectral imagery from the EuroSat dataset and find that the hierarchical approach improves the compatibility of the resulting distributions without sacrificing accuracy.

Original languageEnglish
Pages4011-4014
Number of pages4
DOIs
StatePublished - 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: Jul 12 2021Jul 16 2021

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period7/12/217/16/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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