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  1. Artikel ; Online: Multi-level perception fusion dehazing network.

    Wu, Xiaohua / Li, Zenglu / Guo, Xiaoyu / Xiang, Songyang / Zhang, Yao

    PloS one

    2023  Band 18, Heft 10, Seite(n) e0285137

    Abstract: Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and ...

    Abstract Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and the loss of important information during network sampling, resulting in incomplete or structurally flawed dehazing outcomes. To address these challenges, we propose a multi-level perception fusion dehazing network (MPFDN) that effectively integrates feature information across different scales, expands the perceptual field of the network, and fully extracts the spatial background information of the image. Moreover, we employ an error feedback mechanism and a feature compensator to address the loss of features during the image dehazing process. Finally, we subtract the original hazy image from the generated residual image to obtain a high-quality dehazed image. Based on extensive experimentation, our proposed method has demonstrated outstanding performance not only on synthesizing dehazing datasets, but also on non-homogeneous haze datasets.
    Mesh-Begriff(e) Artificial Intelligence ; Empirical Research ; Recognition, Psychology ; Research Design ; Perception
    Sprache Englisch
    Erscheinungsdatum 2023-10-02
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0285137
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Retrieving chlorophyll content and equivalent water thickness of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

    Xu, Zhanghua / He, Anqi / Zhang, Yiwei / Hao, Zhenbang / Li, Yifan / Xiang, Songyang / Li, Bin / Chen, Lingyan / Yu, Hui / Shen, Wanling / Huang, Xuying / Guo, Xiaoyu / Li, Zenglu

    Forest Ecosystems. 2023, v. 10 p.100108-

    2023  

    Abstract: Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative ... ...

    Abstract Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative transfer model is widely used for vegetation biochemical component content inversion. However, the presence of leaf-eating pests, such as Pantana phyllostachysae Chao (PPC), weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered. Therefore, this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables (LUTs) based on the PROSAIL framework by setting different parameter ranges according to infestation levels. Quantitative inversions of leaf area index (LAI), leaf chlorophyll content (LCC), and leaf equivalent water thickness (LEWT) were derived. The scale conversions from LCC to canopy chlorophyll content (CCC) and LEWT to canopy equivalent water thickness (CEWT) were calculated. The results showed that LAI, CCC, and CEWT were inversely related with PPC-induced stress. When applying multiple LUTs, the p-values were <0.01; the R² values for LAI, CCC, and CEWT were 0.71, 0.68, and 0.65 with root mean square error (RMSE) (normalized RMSE, NRMSE) values of 0.38 (0.16), 17.56 μg·cm‒² (0.20), and 0.02 cm (0.51), respectively. Compared to the values obtained for the traditional PROSAIL model, for October, R² values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT, respectively and RMSE decreased by 0.35 μg·cm‒² for CCC. The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model, while establishing multiple LUTs under different pest-induced damage levels, was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.
    Schlagwörter Phyllostachys edulis ; bamboos ; chlorophyll ; ecosystems ; forest health ; forests ; leaf area index ; leaf chlorophyll content ; leaf reflectance ; model validation ; models ; radiative transfer ; Moso bamboo ; Chlorophyll content ; Equivalent water thickness ; PROSAIL model ; Multiple LUTs ; Pantana phyllostachysae Chao ; Sentinel-2A/B images
    Sprache Englisch
    Erscheinungsort Elsevier B.V.
    Dokumenttyp Artikel ; Online
    Anmerkung Use and reproduction
    ZDB-ID 2760380-5
    ISSN 2197-5620
    ISSN 2197-5620
    DOI 10.1016/j.fecs.2023.100108
    Datenquelle NAL Katalog (AGRICOLA)

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  3. Artikel: Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

    Xu, Zhanghua / Zhang, Qi / Xiang, Songyang / Li, Yifan / Huang, Xuying / Zhang, Yiwei / Zhou, Xin / Li, Zenglu / Yao, Xiong / Li, Qiaosi / Guo, Xiaoyu

    Forests. 2022 Mar. 05, v. 13, no. 3

    2022  

    Abstract: In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a ... ...

    Abstract In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient boosting tree (XGBoost) machine learning algorithms. The overall detection accuracy (OA) and Kappa coefficient of SVM, RF, and XGBoost were 81.95%, 0.733, 85.71%, 0.805, and 86.47%, 0.811, respectively. Meanwhile, the detection accuracies of SVM, RF, and XGBoost were 78.26%, 76.19%, and 80.95% for healthy, 75.00%, 83.87%, and 79.17% for mild damage, 83.33%, 86.49%, and 85.00% for moderate damage, and 82.5%, 90.91%, and 93.75% for severe damage Moso bamboo, respectively. Overall, XGBoost exhibited the best detection performance, followed by RF and SVM. Thus, the study findings provide a technical reference for the regional monitoring and control of PPC in Moso bamboo.
    Schlagwörter Phyllostachys edulis ; forests ; models ; plant pests ; trees ; unmanned aerial vehicles
    Sprache Englisch
    Erscheinungsverlauf 2022-0305
    Erscheinungsort Multidisciplinary Digital Publishing Institute
    Dokumenttyp Artikel
    ZDB-ID 2527081-3
    ISSN 1999-4907
    ISSN 1999-4907
    DOI 10.3390/f13030418
    Datenquelle NAL Katalog (AGRICOLA)

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