Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-10-16 DOI:10.1016/j.ecoinf.2024.102854

Hongran Li , Hui Zhao , Chao Wei , Min Cao , Jian Zhang , Heng Zhang , Dongqing Yuan

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引用次数: 0

Abstract

Water quality assessment is essential for effective environmental management, yet traditional methods such as chemical sampling are often labor-intensive and inefficient for large-scale, continual monitoring. This study addresses these limitations by leveraging hyperspectral images (HSIs) analysis and introducing a capsule network (CapsNet) model enhanced with a multidimensional integration attention (MDIA) mechanism. The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. To validate the performance of the model, spectral data from 5 water quality regions are collected and processed via a UAV-carried spectrometer, with 4503 water quality data samples. Rigorous classification experiments demonstrated that the model achieves 98.73 % accuracy, with an average improvement of 4.89 % compared with the other models. This approach significantly improves decision support systems for water resource management, facilitating the sustainable use of water resources.

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利用高光谱图像和深度学习模型评估水质环境等级：中国江苏案例研究

水质评估对于有效的环境管理至关重要，但传统方法（如化学采样）往往耗费大量人力，而且在大规模持续监测中效率低下。本研究通过利用高光谱图像（HSIs）分析，并引入一个利用多维集成关注（MDIA）机制增强的胶囊网络（CapsNet）模型，解决了这些局限性。该模型专为整合渠道和空间信息而设计，通过检测高光谱图像数据中的细微特征，实现精确的水质等级评估。为了验证该模型的性能，通过无人机携带的光谱仪收集和处理了 5 个水质区域的光谱数据，共 4503 个水质数据样本。严格的分类实验表明，该模型的准确率达到 98.73%，与其他模型相比平均提高了 4.89%。这种方法极大地改进了水资源管理的决策支持系统，促进了水资源的可持续利用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.