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Registration of Multimodal Remote Sensing Image Based on Deep Fully Convolutional Neural Network
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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2019 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 12, no 8, p. 3028-3042, article id 8730416Article in journal (Refereed) Published
Abstract [en]

Multimodal image registration is the fundamental technique for scene analysis with series remote sensing images of different spectrum region. Due to the highly nonlinear radiometric relationship, it is quite challenging to find common features between images of different modal types. This paper resorts to the deep neural network, and tries to learn descriptors for multimodal image patch matching, which is the key issue of image registration. A Siamese fully convolutional network is set up and trained with a novel loss function, which adopts the strategy of maximizing the feature distance between positive and hard negative samples. The two branches of the Siamese network are connected by the convolutional operation, resulting in the similarity score between the two input image patches. The similarity score value is used, not only for correspondence point location, but also for outlier identification. A generalized workflow for deep feature based multimodal RS image registration is constructed, including the training data curation, candidate feature point generation, and outlier removal. The proposed network is tested on a variety of optical, near infrared, thermal infrared, SAR, and map images. Experiment results verify the superiority over other state-of-the-art approaches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 12, no 8, p. 3028-3042, article id 8730416
Keywords [en]
Fully convolutional, hard negative sample, multimodal image registration, Siamese neural network
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-262538DOI: 10.1109/JSTARS.2019.2916560ISI: 000487530100037Scopus ID: 2-s2.0-85072642648OAI: oai:DiVA.org:kth-262538DiVA, id: diva2:1366435
Note

QC 20191029

Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-10-29Bibliographically approved

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Xiang, Deliang

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
  • html
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  • asciidoc
  • rtf