在地中海农业平原上使用深度学习评估基于像素和基于对象的图像分类效率

IF 2.9 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Murat Bayazit, Cenk Dönmez, Süha Berberoglu
{"title":"在地中海农业平原上使用深度学习评估基于像素和基于对象的图像分类效率","authors":"Murat Bayazit,&nbsp;Cenk Dönmez,&nbsp;Süha Berberoglu","doi":"10.1007/s10661-024-13431-2","DOIUrl":null,"url":null,"abstract":"<div><p>Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness. However, the success of deep learning is contingent upon the quality of the training data. In our study, we compared the efficiency of pixel-based and object-based classifications in Sentinel-2 satellite imagery using the Deeplabv3 deep learning method. The image sharpness was enhanced through a high-pass filter, aiding in data visualization and preparation. Deeplabv3 underwent training, leading to the development of classifiers following the extraction of training samples from the enhanced image. The majority zonal statistic method was implemented to assign class values to objects in the workflow. The accuracy of pixel-based and object-based classification was 83.1% and 83.5%, respectively, with corresponding kappa values of 0.786 and 0.791. These accuracies highlighted the efficient performance of the object-based method when integrated with a deep learning classifier. These results can serve as a valuable reference for future studies, aiding in the improvement of accuracy while potentially saving time and effort. By evaluating this nuanced impact pixel and object-based classification as well as on class-specific accuracy, this research contributes to the ongoing refinement of satellite image interpretation techniques in environmental applications.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain\",\"authors\":\"Murat Bayazit,&nbsp;Cenk Dönmez,&nbsp;Süha Berberoglu\",\"doi\":\"10.1007/s10661-024-13431-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness. However, the success of deep learning is contingent upon the quality of the training data. In our study, we compared the efficiency of pixel-based and object-based classifications in Sentinel-2 satellite imagery using the Deeplabv3 deep learning method. The image sharpness was enhanced through a high-pass filter, aiding in data visualization and preparation. Deeplabv3 underwent training, leading to the development of classifiers following the extraction of training samples from the enhanced image. The majority zonal statistic method was implemented to assign class values to objects in the workflow. The accuracy of pixel-based and object-based classification was 83.1% and 83.5%, respectively, with corresponding kappa values of 0.786 and 0.791. These accuracies highlighted the efficient performance of the object-based method when integrated with a deep learning classifier. These results can serve as a valuable reference for future studies, aiding in the improvement of accuracy while potentially saving time and effort. By evaluating this nuanced impact pixel and object-based classification as well as on class-specific accuracy, this research contributes to the ongoing refinement of satellite image interpretation techniques in environmental applications.</p></div>\",\"PeriodicalId\":544,\"journal\":{\"name\":\"Environmental Monitoring and Assessment\",\"volume\":\"197 2\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Monitoring and Assessment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10661-024-13431-2\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Monitoring and Assessment","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10661-024-13431-2","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

最近卫星技术的进步大大扩展了数据采集能力,使卫星图像更容易获得。尽管取得了这些进步,但要充分发挥卫星图像的潜力,就必须进行有效的解译。图像分类被广泛用于提取有价值的信息,由于其有效性,深度学习方法的应用激增。然而,深度学习的成功取决于训练数据的质量。在我们的研究中,我们使用Deeplabv3深度学习方法比较了Sentinel-2卫星图像中基于像素和基于对象的分类效率。通过高通滤波器增强了图像清晰度,有助于数据可视化和准备。deepplabv3经过训练,在从增强图像中提取训练样本后,导致分类器的发展。采用多数分区统计方法对工作流中的对象进行类值分配。基于像素和目标的分类准确率分别为83.1%和83.5%,kappa值分别为0.786和0.791。当与深度学习分类器集成时,这些准确性突出了基于对象的方法的高效性能。这些结果可以为未来的研究提供有价值的参考,有助于提高准确性,同时潜在地节省时间和精力。通过评估这种微妙的影响像素和基于对象的分类以及特定类别的准确性,本研究有助于不断改进卫星图像解译技术在环境应用中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain

Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness. However, the success of deep learning is contingent upon the quality of the training data. In our study, we compared the efficiency of pixel-based and object-based classifications in Sentinel-2 satellite imagery using the Deeplabv3 deep learning method. The image sharpness was enhanced through a high-pass filter, aiding in data visualization and preparation. Deeplabv3 underwent training, leading to the development of classifiers following the extraction of training samples from the enhanced image. The majority zonal statistic method was implemented to assign class values to objects in the workflow. The accuracy of pixel-based and object-based classification was 83.1% and 83.5%, respectively, with corresponding kappa values of 0.786 and 0.791. These accuracies highlighted the efficient performance of the object-based method when integrated with a deep learning classifier. These results can serve as a valuable reference for future studies, aiding in the improvement of accuracy while potentially saving time and effort. By evaluating this nuanced impact pixel and object-based classification as well as on class-specific accuracy, this research contributes to the ongoing refinement of satellite image interpretation techniques in environmental applications.

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
Environmental Monitoring and Assessment
Environmental Monitoring and Assessment 环境科学-环境科学
CiteScore
4.70
自引率
6.70%
发文量
1000
审稿时长
7.3 months
期刊介绍: Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信