Detection of Abnormal Regions in a Field by Semantic Segmentation Using Remote Sensing Data

Muranaka Kenta, Ozawa Seiichi

doi:10.5687/iscie.37.275

Bibliographic Information

Other Title

リモートセンシングデータを用いたセマンティックセグメンテーションによる圃場内異常検出

Description

<p>In recent years, “smart agriculture,” which introduces ICT into agriculture to improve the efﬁciency, automation, and productivity of agricultural work, is attracting attention. For advanced smart agriculture, this paper proposes a semantic segmentation model to detect and classify abnormal regions in a ﬁeld using satellite or aerial images. The performance evaluation of the proposed model is conducted for the Agriculture-Vision Challenge Dataset, a dataset of aerial images of ﬁelds and abnormal regions. The results show that the proposed model can detect abnormal regions with higher performance than conventional deep learning models.</p>

Journal

Transactions of the Institute of Systems, Control and Information Engineers

Transactions of the Institute of Systems, Control and Information Engineers 37 (11), 275-282, 2024-11-15

THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Keywords

Details 詳細情報について

CRID: 1390866183039117056

DOI: 10.5687/iscie.37.275

ISSN: 2185811X; 13425668

Web Site: https://www.jstage.jst.go.jp/article/iscie/37/11/37_275/_pdf

Text Lang: ja

Data Source

JaLC
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Abstract License Flag: Disallowed

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