Výsledky vyhledávání - "Tomoyuki Imaizumi"

CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring

Autor: Riho Ito, Shota Iino, Tomoyuki Imaizumi, Kento Doi, Shuhei Hikosaka

Publikováno v: International Journal of Image and Data Fusion. 9:302-318

Urban areas in developing countries are experiencing rapid growth, and monitoring short-term changes has become increasingly important. For short-term monitoring, constant observation and generation of high-accuracy urban distribution maps without no

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c93d0336403ad38e47249a5eec133522
https://doi.org/10.1080/19479832.2018.1491897

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Urban Change Monitoring in Developing Countries Based on Deep Learning Technique by Utilizing Time Series Imageries of the SAR and Optical Satellites

Autor: Riho Ito, Shota Iino, Shuhei Hikosaka, Tomoyuki Imaizumi

Publikováno v: TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, AEROSPACE TECHNOLOGY JAPAN. 16:40-46

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0e0861bcd7c00ef231e7a4e0411993d2
https://doi.org/10.2322/tastj.16.40

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Classification of Rare Building Change Using CNN with Multi-Class Focal Loss

Autor: Keisuke Nemoto, Shuhei Hikosaka, Ryuhei Hamaguchi, Tomoyuki Imaizumi

Publikováno v: IGARSS

In the remote sensing, supervised deep learning has recently achieved great success of information extraction. However, it requires a large training data in order to effectively learn. In building change classifications, collecting such training data

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5da218cc2148ac9839b2964c9f69b151
https://doi.org/10.1109/igarss.2018.8517563

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Detecting Buildings of any Size Using Integration of Cnn Models

Autor: Keisuke Nemoto, Tomoyuki Imaizumi, Ryuhei Hamaguchi, Shuhei Hikosaka

Publikováno v: IGARSS

Buildings in remote sensing imagery have a wide variation in their size. These buildings of different sizes are also very different in their appearance. Although recent CNN based methods show remarkably high performance in building detection task, pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f89fa777d6e0fbb982574e38f00a200a
https://doi.org/10.1109/igarss.2018.8517887

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Generating high-accuracy urban distribution map for short-term change monitoring based on convolutional neural network by utilizing SAR imagery

Autor: Kento Doi, Shota Iino, Tomoyuki Imaizumi, Riho Ito, Shuhei Hikosaka

Publikováno v: Earth Resources and Environmental Remote Sensing/GIS Applications VIII.

In the developing countries, urban areas are expanding rapidly. With the rapid developments, a short term monitoring of urban changes is important. A constant observation and creation of urban distribution map of high accuracy and without noise pollu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a3068c139cec1d39b66056c6b9fdca51
https://doi.org/10.1117/12.2277901

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Building change detection via a combination of CNNs using only RGB aerial imageries

Autor: Ryuhei Hamaguchi, Keisuke Nemoto, Masakazu Sato, Aito Fujita, Tomoyuki Imaizumi, Shuhei Hikosaka

Publikováno v: Remote Sensing Technologies and Applications in Urban Environments II.

Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3b44004acdd45abb675363be88582f1f
https://doi.org/10.1117/12.2277912

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Damage detection from aerial images via convolutional neural networks

Autor: Ryosuke Nakamura, Aito Fujita, Ken Sakurada, Shuhei Hikosaka, Riho Ito, Tomoyuki Imaizumi

Publikováno v: MVA

This paper explores the effective use of Convolutional Neural Networks (CNNs) in the context of washed-away building detection from pre- and post-tsunami aerial images. To this end, we compile a dedicated, labeled aerial image dataset to construct mo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8fa5ff3f0a6348df1e96fb82426c551b
https://doi.org/10.23919/mva.2017.7986759

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