Object-based multispectral image fusion method using deep learning

Hyunsung Jang, Namkoo Ha, Yoonmo Yeon, Kuyong Kwon, Sungho Gil, Seungha Lee, Sungsoon Park, Hyungjoo Jung, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The goal of multispectral image fusion is to integrate complementary information from multispectral sensors to enhance human visual perception and object detection. Additionally, there are also cases when only the object needs to be emphasized with minimal background interference. This paper presents an object-based fusion method using deep learning to accomplish this objective. The proposed method uses information regarding the region of an object to perform fusion on the object. As we cannot provide labels for fusion results at the learning stage, we propose an unsupervised learning method. The proposed method simultaneously provides appropriate image information from the background and target for surveillance and reconnaissance.

Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning in Defense Applications
EditorsJudith Dijk
PublisherSPIE
ISBN (Electronic)9781510630413
DOIs
Publication statusPublished - 2019
EventArtificial Intelligence and Machine Learning in Defense Applications 2019 - Strasbourg, France
Duration: 2019 Sep 102019 Sep 12

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11169
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceArtificial Intelligence and Machine Learning in Defense Applications 2019
CountryFrance
CityStrasbourg
Period19/9/1019/9/12

Bibliographical note

Funding Information:
This research was supported by the Target Detection and Tracking using the Deep Learning RZ04CM-001) and the SWIR/LWIR Image Fusion project (No. Y17-030) of LIG Nex1 Co., Ltd.

Publisher Copyright:
© 2019 SPIE.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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