APPLICATION OF COMPOSITIONAL NEURAL NETWORKS FOR ROBUST CLASSIFICATION OF INFRARED IMAGERY

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Spell, GP; Collins, LM; Malof, JM

Published Date

  • January 1, 2021

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Volume / Issue

  • 2021-July /

Start / End Page

  • 2799 - 2802

International Standard Book Number 13 (ISBN-13)

  • 9781665403696

Digital Object Identifier (DOI)

  • 10.1109/IGARSS47720.2021.9553370

Citation Source

  • Scopus