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Improving Object Detection of Remotely Sensed Multispectral Imagery Via Pan-sharpening

Authors:

Yuejun SongAuthors Info & Claims

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

Pages 136 - 140

https://doi.org/10.1145/3436369.3437446

Published: 11 January 2021 Publication History

Abstract

Pan-sharpening is used to fuse multispectral images with low spatial resolution and a panchromatic (Pan) image with high spatial resolution to generate synthesized images featured with high spatial and multispectral properties. The pan-sharpened images are assumed valuable for further application. However, there have been a few investigations on the effectiveness of the pan-sharpened products in practice (i.e. object detection), compared with the fact many algorithms for pan-sharpening have been developed. In this paper, improvements contributed by pan-sharpening process for the object detection in multispectral imagery were investigated. Original multispectral images along with the corresponding Pan images acquired by Gaojing-1 (SuperView-1, as the first sub-meter high-resolution commercial remote sensing satellite independently developed in China) satellite were used. Seven algorithms widely used in pan-sharpening were applied separately and compared, while the object detection experiments were done by implementing Faster RCNN. The preliminary findings show: (1) the pan-sharpened images present obviously positive contribution to object detection with Faster RCNN, compared to the original multispectral images; (2) detection results of the pan-sharpened images vary with the algorithms used in pan-sharpening process. Furthermore, this investigation suggests none of the pan-sharpening algorithms showed absolute advantages in image fusion to achieve better object detection consequently.

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Cited By

Modak SHeil JStein A(2024)Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial NetworkRemote Sensing10.3390/rs1605087416:5(874)Online publication date: 1-Mar-2024
https://doi.org/10.3390/rs16050874
Wang PSertel E(2021)Channel–spatial attention-based pan-sharpening of very high-resolution satellite imagesKnowledge-Based Systems10.1016/j.knosys.2021.107324229:COnline publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107324

Index Terms

Improving Object Detection of Remotely Sensed Multispectral Imagery Via Pan-sharpening
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Other conferences

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

October 2020

552 pages

ISBN:9781450387835

DOI:10.1145/3436369

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Beijing University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2021

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Fujian Key Lab on Sensing and Computing for Smart Cities
High level talents research project of Xiamen University of Technology
Jiangxi Provincial Key Laboratory of Soil Erosion and Prevention

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ICCPR 2020

ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition

October 30 - November 1, 2020

Xiamen, China

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Cited By

Modak SHeil JStein A(2024)Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial NetworkRemote Sensing10.3390/rs1605087416:5(874)Online publication date: 1-Mar-2024
https://doi.org/10.3390/rs16050874
Wang PSertel E(2021)Channel–spatial attention-based pan-sharpening of very high-resolution satellite imagesKnowledge-Based Systems10.1016/j.knosys.2021.107324229:COnline publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107324

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