دورية أكاديمية
Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery
| العنوان: | Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery |
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| المؤلفون: | Seok Min Hong, Kyung Hwa Cho, Sanghyun Park, Taegu Kang, Moon Sung Kim, Gibeom Nam, JongCheol Pyo |
| المصدر: | GIScience & Remote Sensing, Vol 59, Iss 1, Pp 547-567 (2022) |
| بيانات النشر: | Taylor & Francis Group, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Mathematical geography. Cartography LCC:Environmental sciences |
| مصطلحات موضوعية: | algal pigment, deep learning model, sensitivity analysis, geum river, nakdong river, yeongsan river, Mathematical geography. Cartography, GA1-1776, Environmental sciences, GE1-350 |
| الوصف: | Although remote sensing techniques have been used to monitor toxic cyanobacteria with hyperspectral data in inland water, it is difficult to optimize conventional bio-optical algorithms for individual water bodies because of the complex optical properties of various water components. Therefore, this study adopted a spatial attention convolutional neural network (spatial attention CNN) to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in the Geum, Nakdong, and Yeongsan rivers in South Korea in order to evaluate cyanobacteria using remote sensing reflectance data. The CNN model utilized a spatial attention module to analyze the importance of the bands in the reflectance data. Then, the spatial attention CNN model was compared with different bio-optical algorithms for each study area. The spatial attention CNN model was generalized to estimate the pigment concentrations in the target rivers, and the model performance was evaluated by correlation coefficient (R) and root mean squared error (RMSE) between the observed and estimated concentrations of the algal pigments. The spatial attention CNN model, which was generalized to estimate the pigment concentrations in the target rivers, had R values above 0.87 and 0.88 for Chl-a and PC, respectively. However, the optimized band ratio algorithms for Chl-a and PC had R values above 0.83 and 0.70, respectively. Hence, it showed better performance than the conventional bio-optical algorithms. The spatial attention module provided attention weights for visualizing important features in the reflectance data. Specifically, the 600 nm, 650 nm, and near-infrared regions had high attention weights for estimating the concentrations of Chl-a and PC. Based on these findings, this study demonstrated that the spatial attention CNN model has a high potential for good application performance in various water bodies. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1548-1603 1943-7226 15481603 |
| Relation: | https://doaj.org/toc/1548-1603; https://doaj.org/toc/1943-7226 |
| DOI: | 10.1080/15481603.2022.2037887 |
| URL الوصول: | https://doaj.org/article/2e9fc947303843f182f55f7a6410b5ce |
| رقم الأكسشن: | edsdoj.2e9fc947303843f182f55f7a6410b5ce |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 15481603 19437226 |
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| DOI: | 10.1080/15481603.2022.2037887 |