科大讯飞挑战赛2021从高分辨率遥感图像中提取耕地

Z. Zhao, Yuqiu Liu, Gang Zhang, Liang Tang, Xiao-Ning Hu
{"title":"科大讯飞挑战赛2021从高分辨率遥感图像中提取耕地","authors":"Z. Zhao, Yuqiu Liu, Gang Zhang, Liang Tang, Xiao-Ning Hu","doi":"10.1109/ICACI55529.2022.9837765","DOIUrl":null,"url":null,"abstract":"Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This paper introduces our solution to iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segmentation algorithms that work well on natural images and developed a set of effective methods that are applicable to remote sensing images. Then we merged the prediction results of all small tiles into seamless, continuous segmentation results through our proposed overlap-tile fusion strategy. We achieved first place among 486 teams in the challenge.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Images\",\"authors\":\"Z. Zhao, Yuqiu Liu, Gang Zhang, Liang Tang, Xiao-Ning Hu\",\"doi\":\"10.1109/ICACI55529.2022.9837765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This paper introduces our solution to iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segmentation algorithms that work well on natural images and developed a set of effective methods that are applicable to remote sensing images. Then we merged the prediction results of all small tiles into seamless, continuous segmentation results through our proposed overlap-tile fusion strategy. We achieved first place among 486 teams in the challenge.\",\"PeriodicalId\":412347,\"journal\":{\"name\":\"2022 14th International Conference on Advanced Computational Intelligence (ICACI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 14th International Conference on Advanced Computational Intelligence (ICACI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACI55529.2022.9837765\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACI55529.2022.9837765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

从高分辨率遥感影像中准确提取耕地是精准农业的一项基本任务。本文介绍了科大讯飞挑战2021高分辨率遥感影像耕地提取的解决方案。我们建立了一个高效的管道来解决这个问题。我们首先将原始图像分割成小块,并对每个小块分别进行实例分割。我们探索了几种适用于自然图像的实例分割算法,并开发了一套适用于遥感图像的有效方法。然后通过我们提出的重叠块融合策略,将所有小块的预测结果合并为无缝连续的分割结果。我们在486支队伍中获得了第一名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Images
Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This paper introduces our solution to iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segmentation algorithms that work well on natural images and developed a set of effective methods that are applicable to remote sensing images. Then we merged the prediction results of all small tiles into seamless, continuous segmentation results through our proposed overlap-tile fusion strategy. We achieved first place among 486 teams in the challenge.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Speed Estimation of Video Target Based on Siamese Convolutional Network and Kalman Filtering Aspect Term Extraction and Categorization for Chinese MOOC Reviews A Global Harmony Search Algorithm Based on Tent Chaos Map and Elite Reverse Learning An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks New Results on Finite-Time Synchronization of Delayed Fuzzy Neural Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1