Multi-temporal registration of environmental imagery using affine invariant convolutional features

Asim Khan; Anwaar Ulhaq; Randall W. Robinson

doi:10.1007/978-3-030-34879-3_21

Multi-temporal registration of environmental imagery using affine invariant convolutional features

Asim Khan, Anwaar Ulhaq, Randall W. Robinson

Self-care Policy Advisory Panel

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

Repeat photography is a practice of collecting multiple images of the same subject at the same location but at different timestamps for comparative analysis. The visualisation of such imagery can provide a valuable insight for continuous monitoring and change detection. In Victoria, Australia, citizen science and environmental monitoring are integrated through the visitor-based repeat photography of national parks and coastal areas. Repeat photography, however, poses enormous challenges for automated data analysis and visualisation due to variations in viewpoints, scales, luminosity and camera attributes. To address these challenges brought by data variability, this paper introduces a robust multi-temporal image registration approach based on affine invariance and convolutional neural network architecture. Our experimental evaluation on a large repeat photography dataset validates the role of multi-temporal image registration for better visualisation of environmental monitoring imagery. Our research will establish a baseline for the broad area of multi-temporal analysis.

Original language	English
Title of host publication	Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings
Editors	Chilwoo Lee, Zhixun Su, Akihiro Sugimoto
Publisher	Springer
Pages	269-280
Number of pages	12
ISBN (Electronic)	9783030348793
ISBN (Print)	9783030348786
DOIs	https://doi.org/10.1007/978-3-030-34879-3_21
Publication status	Published - 11 Nov 2019
Event	The 9th Pacific-Rim Symposium on Image and Video Technology - Charles Sturt University Study Centre, Sydney, Australia Duration: 18 Nov 2019 → 22 Nov 2019 http://www.psivt.org/psivt2019/ http://www.psivt.org/psivt2019/program.html (program)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11854 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 9th Pacific-Rim Symposium on Image and Video Technology
Country/Territory	Australia
City	Sydney
Period	18/11/19 → 22/11/19
Other	The Pacific-Rim Symposium on Image and Video Technology (PSIVT) is a premier level biennial series of symposia that aim at providing a forum for researchers and practitioners who are being involved, or are contributing to theoretical advances or practical implementations in image and video technology. The ninth Pacific-Rim Symposium on Image and Video Technology (PSIVT 2019) will be held at Sydney, Australia from 18th to 22nd November, 2019.
Internet address	http://www.psivt.org/psivt2019/ http://www.psivt.org/psivt2019/program.html (program)

Access to Document

10.1007/978-3-030-34879-3_21

Cite this

Khan, A., Ulhaq, A., & Robinson, R. W. (2019). Multi-temporal registration of environmental imagery using affine invariant convolutional features. In C. Lee, Z. Su, & A. Sugimoto (Eds.), Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings (pp. 269-280). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11854 LNCS). Springer. https://doi.org/10.1007/978-3-030-34879-3_21

Khan, Asim ; Ulhaq, Anwaar ; Robinson, Randall W. / Multi-temporal registration of environmental imagery using affine invariant convolutional features. Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings. editor / Chilwoo Lee ; Zhixun Su ; Akihiro Sugimoto. Springer, 2019. pp. 269-280 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{59de66727678412a995579a3a030a1fc,

title = "Multi-temporal registration of environmental imagery using affine invariant convolutional features",

abstract = "Repeat photography is a practice of collecting multiple images of the same subject at the same location but at different timestamps for comparative analysis. The visualisation of such imagery can provide a valuable insight for continuous monitoring and change detection. In Victoria, Australia, citizen science and environmental monitoring are integrated through the visitor-based repeat photography of national parks and coastal areas. Repeat photography, however, poses enormous challenges for automated data analysis and visualisation due to variations in viewpoints, scales, luminosity and camera attributes. To address these challenges brought by data variability, this paper introduces a robust multi-temporal image registration approach based on affine invariance and convolutional neural network architecture. Our experimental evaluation on a large repeat photography dataset validates the role of multi-temporal image registration for better visualisation of environmental monitoring imagery. Our research will establish a baseline for the broad area of multi-temporal analysis.",

keywords = "Deep learning, Descriptors, Image registration, Repeat photography",

author = "Asim Khan and Anwaar Ulhaq and Robinson, {Randall W.}",

year = "2019",

month = nov,

day = "11",

doi = "10.1007/978-3-030-34879-3_21",

language = "English",

isbn = "9783030348786",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "269--280",

editor = "Chilwoo Lee and Zhixun Su and Akihiro Sugimoto",

booktitle = "Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings",

address = "United States",

note = "The 9th Pacific-Rim Symposium on Image and Video Technology ; Conference date: 18-11-2019 Through 22-11-2019",

url = "http://www.psivt.org/psivt2019/, http://www.psivt.org/psivt2019/program.html",

}

Khan, A, Ulhaq, A & Robinson, RW 2019, Multi-temporal registration of environmental imagery using affine invariant convolutional features. in C Lee, Z Su & A Sugimoto (eds), Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11854 LNCS, Springer, pp. 269-280, The 9th Pacific-Rim Symposium on Image and Video Technology, Sydney, New South Wales, Australia, 18/11/19. https://doi.org/10.1007/978-3-030-34879-3_21

Multi-temporal registration of environmental imagery using affine invariant convolutional features. / Khan, Asim; Ulhaq, Anwaar; Robinson, Randall W.
Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings. ed. / Chilwoo Lee; Zhixun Su; Akihiro Sugimoto. Springer, 2019. p. 269-280 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11854 LNCS).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Multi-temporal registration of environmental imagery using affine invariant convolutional features

AU - Khan, Asim

AU - Ulhaq, Anwaar

AU - Robinson, Randall W.

PY - 2019/11/11

Y1 - 2019/11/11

N2 - Repeat photography is a practice of collecting multiple images of the same subject at the same location but at different timestamps for comparative analysis. The visualisation of such imagery can provide a valuable insight for continuous monitoring and change detection. In Victoria, Australia, citizen science and environmental monitoring are integrated through the visitor-based repeat photography of national parks and coastal areas. Repeat photography, however, poses enormous challenges for automated data analysis and visualisation due to variations in viewpoints, scales, luminosity and camera attributes. To address these challenges brought by data variability, this paper introduces a robust multi-temporal image registration approach based on affine invariance and convolutional neural network architecture. Our experimental evaluation on a large repeat photography dataset validates the role of multi-temporal image registration for better visualisation of environmental monitoring imagery. Our research will establish a baseline for the broad area of multi-temporal analysis.

AB - Repeat photography is a practice of collecting multiple images of the same subject at the same location but at different timestamps for comparative analysis. The visualisation of such imagery can provide a valuable insight for continuous monitoring and change detection. In Victoria, Australia, citizen science and environmental monitoring are integrated through the visitor-based repeat photography of national parks and coastal areas. Repeat photography, however, poses enormous challenges for automated data analysis and visualisation due to variations in viewpoints, scales, luminosity and camera attributes. To address these challenges brought by data variability, this paper introduces a robust multi-temporal image registration approach based on affine invariance and convolutional neural network architecture. Our experimental evaluation on a large repeat photography dataset validates the role of multi-temporal image registration for better visualisation of environmental monitoring imagery. Our research will establish a baseline for the broad area of multi-temporal analysis.

KW - Deep learning

KW - Descriptors

KW - Image registration

KW - Repeat photography

UR - http://www.scopus.com/inward/record.url?scp=85076392265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076392265&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-34879-3_21

DO - 10.1007/978-3-030-34879-3_21

M3 - Conference paper

AN - SCOPUS:85076392265

SN - 9783030348786

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 269

EP - 280

BT - Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings

A2 - Lee, Chilwoo

A2 - Su, Zhixun

A2 - Sugimoto, Akihiro

PB - Springer

T2 - The 9th Pacific-Rim Symposium on Image and Video Technology

Y2 - 18 November 2019 through 22 November 2019

ER -

Khan A, Ulhaq A, Robinson RW. Multi-temporal registration of environmental imagery using affine invariant convolutional features. In Lee C, Su Z, Sugimoto A, editors, Image and Video Technology - 9th Pacific-Rim Symposium, PSIVT 2019, Proceedings. Springer. 2019. p. 269-280. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34879-3_21

Multi-temporal registration of environmental imagery using affine invariant convolutional features

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this