Dense Network-Based Spectral-Polarization Image Fusion: Multispectral Data Enhancement via Encoder-Decoder Approach

Shahid Karim, Geng Tong, Jinyang Li, Yiting Yu, Muhammad Ibrar, Faisal Mehmood

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

Abstract

Images are obtained by perceiving the sunlight reflected by objects or scenes, and due to limited solar irradiance, spatial resolution certainly decreases. In contrast, multispectral sensors hold the spatial information of ground objects even though the obtained images only have a few bands, which means that the obtained images have high spatial but insufficient spectral resolution. To overcome these limitations, we have developed a spectral and polarization image fusion technique that improves several perspectives of image fusion, such as intensity, edges, textures, and substantial information. Our proposed method utilizes an encoder-decoder approach, which contains dense and convolutional blocks to simulate the source images. The polarization images have been used to develop DoLP images that help to improve fused images' quality. We can perceive the visual quality of fused images from the experimental results.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Information Technologies and Electrical Engineering, ICITEE 2023
EditorsSrikanta Patnaik
PublisherAssociation for Computing Machinery
Pages441-446
Number of pages6
ISBN (Electronic)9798400708299
DOIs
StatePublished - 3 Nov 2023
Event6th International Conference on Information Technologies and Electrical Engineering, ICITEE 2023 - Changde, China
Duration: 3 Nov 20235 Nov 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th International Conference on Information Technologies and Electrical Engineering, ICITEE 2023
Country/TerritoryChina
CityChangde
Period3/11/235/11/23

Keywords

  • Dense module
  • Image fusion
  • Polarization images
  • Spectral images

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