Exploiting convolutional representations for multiscale human settlement detection: Preliminary results

Dalton Lunga; Dilip Patlolla; Hsiuhan Lexie Yang; Jeanette Weaver; Budhendra Bhadhuri

doi:10.1109/IGARSS.2017.8127822

Exploiting convolutional representations for multiscale human settlement detection: Preliminary results

Dalton Lunga, Dilip Patlolla, Hsiuhan Lexie Yang, Jeanette Weaver, Budhendra Bhadhuri

GeoAI

Oak Ridge National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different settlement representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.

Original language	English
Title of host publication	2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	International Cooperation for Global Awareness, IGARSS 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3779-3782
Number of pages	4
ISBN (Electronic)	9781509049516
DOIs	https://doi.org/10.1109/IGARSS.2017.8127822
State	Published - Dec 1 2017
Event	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States Duration: Jul 23 2017 → Jul 28 2017

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2017-July

Conference

Conference	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/Territory	United States
City	Fort Worth
Period	07/23/17 → 07/28/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Convolutional neural networks
Inductive transfer learning
Representation learning
Segmentation
Settlement mapping

Access to Document

10.1109/IGARSS.2017.8127822

Cite this

Lunga, D., Patlolla, D., Yang, H. L., Weaver, J., & Bhadhuri, B. (2017). Exploiting convolutional representations for multiscale human settlement detection: Preliminary results. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings (pp. 3779-3782). Article 8127822 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2017.8127822

Lunga, Dalton ; Patlolla, Dilip ; Yang, Hsiuhan Lexie et al. / Exploiting convolutional representations for multiscale human settlement detection : Preliminary results. 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3779-3782 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{ee1c8dcb34474d94b46cd622c682d8bc,

title = "Exploiting convolutional representations for multiscale human settlement detection: Preliminary results",

abstract = "We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different settlement representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.",

keywords = "Convolutional neural networks, Inductive transfer learning, Representation learning, Segmentation, Settlement mapping",

author = "Dalton Lunga and Dilip Patlolla and Yang, {Hsiuhan Lexie} and Jeanette Weaver and Budhendra Bhadhuri",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",

year = "2017",

month = dec,

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doi = "10.1109/IGARSS.2017.8127822",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Lunga, D, Patlolla, D, Yang, HL, Weaver, J & Bhadhuri, B 2017, Exploiting convolutional representations for multiscale human settlement detection: Preliminary results. in 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings., 8127822, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, Institute of Electrical and Electronics Engineers Inc., pp. 3779-3782, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, United States, 07/23/17. https://doi.org/10.1109/IGARSS.2017.8127822

Exploiting convolutional representations for multiscale human settlement detection: Preliminary results. / Lunga, Dalton; Patlolla, Dilip; Yang, Hsiuhan Lexie et al.
2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3779-3782 8127822 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different settlement representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.

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KW - Inductive transfer learning

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KW - Segmentation

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Lunga D, Patlolla D, Yang HL, Weaver J, Bhadhuri B. Exploiting convolutional representations for multiscale human settlement detection: Preliminary results. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3779-3782. 8127822. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2017.8127822

Exploiting convolutional representations for multiscale human settlement detection: Preliminary results

Abstract

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Keywords

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