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李慧芳、博士生罗爽的论文在 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 刊出
发布时间:2021-06-30 17:32:10     发布者:易真     浏览次数:

标题: ESPFNet: An Edge-Aware Spatial Pyramid Fusion Network for Salient Shadow Detection in Aerial Remote Sensing Images

作者: Luo, S (Luo, Shuang); Li, HF (Li, Huifang); Zhu, RZ (Zhu, Ruzhao); Gong, YT (Gong, Yuting); Shen, HF (Shen, Huanfeng)

来源出版物: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING : 14 : 4633-4646 DOI: 10.1109/JSTARS.2021.3066791 出版年: 2021

摘要: Shadows can hinder image interpretation in aerial remote sensing images. The existing shadow detection methods focus on all shadow regions and detect the shadow regions directly, but they ignore the fact that salient shadows have a more significant effect. In this work, a novel edge-aware spatial pyramid fusion network (ESPFNet) under a multitask learning framework is proposed for salient shadow detection in aerial remote sensing images. ESPFNet has three components: a parallel spatial pyramid (PSP) structure; an edge detection module (EDM); and an edge-aware multibranch integration (EMI). The PSP structure is constructed to extract multiscale features from the input image and fuse them gradually. The EDM then integrates the shallow features and deep features to detect the shadow edges. Finally, the EMI incorporates the edge features with multibranch features, and then concatenates them with the shallow features to generate the salient shadow detection result. The experimental analyses confirm the effectiveness of the ESPFNet method in both the qualitative and quantitative performance, compared to the existing methods, with the F-score reaching 92.04% in the salient shadow test set.

入藏号: WOS:000652787700002

语言: English

文献类型: Article

作者关键词: Feature extraction; Image edge detection; Remote sensing; Task analysis; Electromagnetic interference; Data mining; Buildings; Aerial remote sensing images; convolutional neural network; multitask learning; salient shadow detection

地址: [Luo, Shuang; Li, Huifang; Gong, Yuting; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Zhu, Ruzhao] KylinSoft Co Ltd, Changsha 410073, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

通讯作者地址: Li, HF (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: sluo@whu.edu.cn; huifangli@whu.edu.cn; zhuruzhao@kylinos.cn; yutinggong@whu.edu.cn; shenhf@whu.edu.cn

影响因子:3.827


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