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Raveerat Jaturapitpornchai
Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan

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Journal article
Published: 19 March 2020 in Remote Sensing
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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.

ACS Style

Raveerat Jaturapitpornchai; Poompat Rattanasuwan; Masashi Matsuoka; Ryosuke Nakamura. CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection. Remote Sensing 2020, 12, 990 .

AMA Style

Raveerat Jaturapitpornchai, Poompat Rattanasuwan, Masashi Matsuoka, Ryosuke Nakamura. CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection. Remote Sensing. 2020; 12 (6):990.

Chicago/Turabian Style

Raveerat Jaturapitpornchai; Poompat Rattanasuwan; Masashi Matsuoka; Ryosuke Nakamura. 2020. "CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection." Remote Sensing 12, no. 6: 990.

Journal article
Published: 18 June 2019 in Remote Sensing
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Remote sensing data can be utilized to help developing countries monitor the use of land. However, the problem of constant cloud coverage prevents us from taking full advantage of satellite optical images. Therefore, we instead opt to use data from synthetic-aperture radar (SAR), which can capture images of the Earth’s surface regardless of the weather conditions. In this study, we use SAR data to identify newly built constructions. Most studies on change detection tend to detect all of the changes that have a similar temporal change characteristic occurring on two occasions, while we want to identify only the constructions and avoid detecting other changes such as the seasonal change of vegetation. To do so, we study various deep learning network techniques and have decided to propose the fully convolutional network with a skip connection. We train this network with pairs of SAR data acquired on two different occasions from Bangkok and the ground truth, which we manually create from optical images available from Google Earth for all of the SAR pairs. Experiments to assign the most suitable patch size, loss weighting, and epoch number to the network are discussed in this paper. The trained model can be used to generate a binary map that indicates the position of these newly built constructions precisely with the Bangkok dataset, as well as with the Hanoi and Xiamen datasets with acceptable results. The proposed model can even be used with SAR images of the same specific satellite from another orbit direction and still give promising results.

ACS Style

Raveerat Jaturapitpornchai; Masashi Matsuoka; Naruo Kanemoto; Shigeki Kuzuoka; Riho Ito; Ryosuke Nakamura. Newly Built Construction Detection in SAR Images Using Deep Learning. Remote Sensing 2019, 11, 1444 .

AMA Style

Raveerat Jaturapitpornchai, Masashi Matsuoka, Naruo Kanemoto, Shigeki Kuzuoka, Riho Ito, Ryosuke Nakamura. Newly Built Construction Detection in SAR Images Using Deep Learning. Remote Sensing. 2019; 11 (12):1444.

Chicago/Turabian Style

Raveerat Jaturapitpornchai; Masashi Matsuoka; Naruo Kanemoto; Shigeki Kuzuoka; Riho Ito; Ryosuke Nakamura. 2019. "Newly Built Construction Detection in SAR Images Using Deep Learning." Remote Sensing 11, no. 12: 1444.