In order to build inversion model of dust-fall weight by hyperspectral data, 30 samples were collected in Beijing. Through electronic balance and Analytical Spectral Devices FieldSpec Pro (ASD) analysis, the "dust leaves" and the "clean leaves" weight and spectral reflectance were determined respectively, which also obtained information of dust weight and spectral features. Then, based on tradition and partial least squares (PLS) model's analysis, the relationship between dust weight and spectral reflectance was explored. The results showed that 350-700, 780-1 300 and 1 900-2 500 nm bands had apparently variations when they response to the different dust weights. In general, there was a negative relationship between dust weight and spectral reflectance, the maximum negative value -0.8 occurred at 737 band which belonged to near-infrared bands. In the analysis of dust weight with multi-band, it was indicated that NDVI index which was formed by 948 and 945 bands had a significant correlation (r = 0.76) to dust. Finally, through accuracy assessment of regression model, the PLS could obtain a more accurate result than the traditional model.
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Sci Rep
January 2025
School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result.
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Shanghai Municipal Institute of Surveying and Mapping, Shanghai, 200063, China.
Inland waters face multiple threats from human activities and natural factors, leading to frequent water quality issues, particularly the significant challenge of eutrophication. Hyperspectral remote sensing provides rich spectral information, enabling timely and accurate assessment of water quality status and trends. To address the challenge of inaccurate water quality mapping, we propose a novel deep learning framework for multi-parameter estimation from hyperspectral imagery.
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January 2025
Nanoscience Research Laboratory, Department of Chemistry, Shivaji University Kolhapur 416 004 Maharashtra India
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Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
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