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Open AccessArticle

CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection

1
Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan
2
Department of Information and Communication Engineering, Tokyo Institute of Technology, Yokohama 226-8503, Japan
3
National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 990; https://doi.org/10.3390/rs12060990
Received: 7 February 2020 / Revised: 10 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Deep Neural Networks for Remote Sensing Applications)
The limitations in obtaining sufficient datasets for training deep learning networks is preventing many applications from achieving accurate results, especially when detecting new constructions using time-series satellite imagery, since this requires at least two images of the same scene and it must contain new constructions in it. To tackle this problem, we introduce Chronological Order Reverse Network (CORN)—an architecture for detecting newly built constructions in time-series SAR images that does not require a large quantity of training data. The network uses two U-net adaptations to learn the changes between images from both Time 1–Time 2 and Time 2–Time 1 formats, which allows it to learn double the amount of changes in different perspectives. We trained the network with 2028 pairs of 256 × 256 pixel SAR images from ALOS-PALSAR, totaling 4056 pairs for the network to learn from, since it learns from both Time 1–Time 2 and Time 2–Time 1. As a result, the network can detect new constructions more accurately, especially at the building boundary, compared to the original U-net trained by the same amount of training data. The experiment also shows that the model trained with CORN can be used with images from Sentinel-1. The source code is available at https://github.com/Raveerat-titech/CORN. View Full-Text
Keywords: satellite imagery; SAR; deep learning; U-net; urban change satellite imagery; SAR; deep learning; U-net; urban change
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MDPI and ACS Style

Jaturapitpornchai, R.; Rattanasuwan, P.; Matsuoka, M.; Nakamura, R. CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection. Remote Sens. 2020, 12, 990.

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