改进DeepLab v3+模型下的梯田遥感提取研究（《智慧农业(中英文)》2024年第3期）

市场资讯2025-12-09

（来源：智慧农业期刊）引用格式：张俊, 陈雨艳, 秦震宇, 张梦瑶, 张军. 改进DeepLab v3+模型下的梯田遥感提取研究[J]. 智慧农业(中英文), 2024, 6(3): 46-57.DOI：10.12133/j.smartag.SA202312028Citation: ZHANG Jun, CHEN Yuyan, QIN Zhenyu, ZHANG Mengyao, ZHANG ...

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Remote Sensing Extraction Method of Terraced Fields Based on Improved DeepLab v3+[J]. Smart Agriculture, 2024, 6(3): 46-57.DOI：10.12133/j.smartag.SA202312028改进DeepLab v3+模型下的梯田遥感提取研究张俊1， 陈雨艳1， 秦震宇2， 张梦瑶1， 张军1*（1.云南大学 地球科学学院，云南昆明 650500，中国；2.云南大学 国际河流与生态安全研究院，云南昆明 650500，中国）摘要： ［目的与意义］ 梯田作为农业生产的关键要素之一，其面积估算对于农业政策制定、土地规划和资源管理至关重要。为解决复杂的地形条件、种植环境导致传统遥感数据和监测方法难以开展梯田自动化提取问题，探索一种利用深度学习技术在高分辨率遥感影像中精准提取梯田面积的方法。［方法］以休耕期梯田高分六号影像构建语义分割数据集，同时提出一种改进的DeepLab v3+模型。该模型使用轻量级网络MobileNet v2作为骨干网络，为了同时兼顾局部细节和全局语境，使用多尺度特征融合（Multi-scale Feature Fusion module, MSFF）模块代替空洞空间金字塔池化（Atrous Spatial Pyramid Pooling, ASPP）模块，利用扩张率依次增大的空洞卷积级联模式改善信息丢失的问题。此外，对浅层特征和深层特征使用坐标注意力机制以加强网络对于目标的学习。［结果与讨论］ 利用红、绿和近红外波段组合方式在梯田提取的精度和效果上表现最佳。相比于原始DeepLab v3+网络，精确率、召回率、F1评分和交并比指标分别提升4.62%、2.61%、3.81%和2.81%。此外，与UNet和原始DeepLab v3+相比，改进的DeepLab v3+在参数量上和浮点运算数有着更为优越的性能，其参数量仅为UNet的28.6%和原始DeepLab v3+的19.5%，同时浮点运算数仅为UNet和DeepLab v3+的1/5。这不仅提高了计算效率，也使得改进后的模型更适用于资源有限或计算能力较低的环境中。［结论］深度学习在高分辨率遥感影像梯田识别中具有较高的精度，有利于为梯田精细化监测和管理提供参考依据。关键词： 梯田提取；遥感；卷积神经网络；高分六号卫星；DeepLab v3+图1　元阳县位置及样本分布Fig.1  Location and sample distribution in Yuanyang county图2　梯田识别数据集构建流程Fig. 2  The process of constructing the terrace identification dataset图3　改进后的DeepLab V3+结构图Fig. 3  Structure of improved DeepLab V3+图4　不同波段组合下测试区域梯田提取结果Fig. 4  Extraction results of terraced fields in test areas under different band combinations图5　元阳县梯田和典型区域识别结果Fig. 5  Results of identification of terraces and typical areas in Yuanyang county图6　元阳县梯田在不同坡度的空间分布Fig. 6  The spatial distribution of terraced fields in Yuanyang county across different slopes图7　元阳县梯田在不同高程的空间分布Fig. 7  The spatial distribution of terraced fields in Yuanyang county across different elevations图8　不同算法提取结果的局部细节对比Fig. 8  Comparison of local details in extraction results obtained using different algorithms作者简介张军 副研究员张军，博士，副研究员，现为云南大学地球科学学院硕士生导师，中国遥感应用协会理事。主要从事遥感信息提取、境外罂粟替代种植遥感监测等方向的教学与科研工作。在《IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing》《农业工程学报》《林业科学》《测绘科学》等期刊发表论文20余篇，作为主要完成人获得高分专项卫星应用优秀成果奖三等奖、完成的境外罂粟替代项目为学习强国所登载。来源：《智慧农业（中英文）》2024年第3期\n\n\n海量资讯、精准解读，尽在新浪财经APP","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":1,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)","isDev":false,"isTTM":false,"tenantId":"TBCN","deviceId":"web-server-community-laohu8-v3","version":"4.42.0","shortVersion":"4.42.0","platform":"web","vendor":"web","appName":"laohu8","isIOS":false,"isAndroid":false,"isTiger":false,"isTHS":false,"isWeiXin":false,"isWeiXinMini":false,"isWeiBo":false,"isQQ":false,"isBaiduSwan":false,"isBaiduBox":false,"isDingTalk":false,"isToutiao":false,"isOnePlus":false,"isHuaWei":false,"isXiaomi":false,"isXiaomiWebView":false,"isOppo":false,"isVivo":false,"isSamsung":false,"isMobile":false},"href":"/m/news/2590341945","isCrawlerRequest":true}