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Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples.
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.
View Article and Find Full Text PDFEnhancing spectral imaging with multi-condition image fusion.
Sci Rep
January 2025
Centre for Applied Photonics, INESC TEC, Rua do Campo Alegre 687, 4169-007, Porto, Portugal.
Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This significantly expands the accessible information compared to conventional imaging approaches such as machine vision. However, despite its potential, spectral imaging also faces specific challenges depending on the limitations of the spectroscopy technique used, such as signal saturation, matrix interferences, fluorescence, or background emission.
View Article and Find Full Text PDFOn latent dynamics learning in nonlinear reduced order modeling.
Neural Netw
January 2025
MOX, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy. Electronic address:
In this work, we present the novel mathematical framework of latent dynamics models (LDMs) for reduced order modeling of parameterized nonlinear time-dependent PDEs. Our framework casts this latter task as a nonlinear dimensionality reduction problem, while constraining the latent state to evolve accordingly to an unknown dynamical system. A time-continuous setting is employed to derive error and stability estimates for the LDM approximation of the full order model (FOM) solution.
View Article and Find Full Text PDFAssessing the influence of the modifiable areal unit problem on Bayesian disease mapping in Queensland, Australia.
PLoS One
January 2025
QUT Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Background: Spatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas.
View Article and Find Full Text PDFImage reconstruction for compressed ultrafast photography based on manifold learning and the alternating direction method of multipliers.
J Opt Soc Am A Opt Image Sci Vis
August 2024
Compressed ultrafast photography (CUP) is a high-speed imaging technique with a frame rate of up to ten trillion frames per second (fps) and a sequence depth of hundreds of frames. This technique is a powerful tool for investigating ultrafast processes. However, since the reconstruction process is an ill-posed problem, the image reconstruction will be more difficult with the increase of the number of reconstruction frames and the number of pixels of each reconstruction frame.
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