The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series

Research output: Contribution to journal › Article (Academic Journal) › peer-review

14 Citations (Scopus)

14 Downloads (Pure)

Original language	English
Journal	Geophysical Research Letters
DOIs	https://doi.org/10.1029/2019GL084993
Publication status	Published - 16 Nov 2019

Access to Document

Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via AGU at https://doi.org/10.1029/2019GL084993 . Please refer to any applicable terms of use of the publisher.
Final published version, 15.5 MBLicence: CC BY

@article{850b5aa1581447adb65bf7310d2e1695,

title = "The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series",

author = "N. Anantrasirichai and J. Biggs and F. Albino and D. Bull",

year = "2019",

month = nov,

day = "16",

doi = "10.1029/2019GL084993",

language = "English",

journal = "Geophysical Research Letters",

issn = "0094-8276",

publisher = "American Geophysical Union",

}

TY - JOUR

T1 - The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series

AU - Anantrasirichai, N.

AU - Biggs, J.

AU - Albino, F.

AU - Bull, D.

PY - 2019/11/16

Y1 - 2019/11/16

UR - https://doi.org/10.1029/2019GL084993

U2 - 10.1029/2019GL084993

DO - 10.1029/2019GL084993

M3 - Article (Academic Journal)

JO - Geophysical Research Letters

JF - Geophysical Research Letters

SN - 0094-8276

ER -