APPLICATION OF COMPOSITIONAL NEURAL NETWORKS FOR ROBUST CLASSIFICATION OF INFRARED IMAGERY

Gregory P. Spell, Leslie M. Collins, Jordan M. Malof

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Thermal infrared (IR) imaging has increasingly been used for remote sensing applications, which has required the adaptation of processing techniques for “natural images” (e.g., RGB images) to this unique domain in order to accommodate such differences as texture and resolution. While Convolutional Neural Networks (CNNs) have recently been shown to perform well for classification of IR images, we consider the common scenario in which the target object is partially occluded. Recent work demonstrates that deep CNNs struggle to generalize under occlusion, and Compositional CNNs (CompNets) have been proposed as deep models to mitigate this shortcoming. In this work, we apply CompNets to IR imagery, discuss the considerations in moving from natural images to IR, and analyze performance on a dataset that has been artificially occluded. Our results indicate that CompNets do, indeed, bolster robustness to occlusion in the IR domain.

Original languageEnglish
Title of host publicationIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2799-2802
Number of pages4
ISBN (Electronic)9781665403696
DOIs
StatePublished - 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: Jul 12 2021Jul 16 2021

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2021-July

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period07/12/2107/16/21

Keywords

  • Compositional model
  • Convolutional neural network
  • Deep learning
  • Infrared imagery

Fingerprint

Dive into the research topics of 'APPLICATION OF COMPOSITIONAL NEURAL NETWORKS FOR ROBUST CLASSIFICATION OF INFRARED IMAGERY'. Together they form a unique fingerprint.

Cite this