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Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer's multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

Objective: To evaluate and compare the image quality of T2-weighted abdominal scans using a respiratory belt trigger (RBT) and frequency- modulated continuous wave (FMCW)-trigger (FT) techniques and to explore the feasibility of FT in abdominal magnetic resonance imaging (MRI). Methods: The study prospectively included 28 subjects, each undergoing abdominal scans with both RBT and FT. The analysis focused on 64 inconsistent trigger segments from the respiratory curves triggered by RBT and FMCW. Parameters such as inconsistent trigger type (ITT), number of inconsistent trigger points (ITPs), ratio of ITP (ITR), and single-segment ITR (SITR) were derived from these curves. Image quality was evaluated by two observers using subjective scoring, signal-to-noise ratio (SNR), and contrast- to-noise ratio (CNR). The assessments classified image quality as either "good" or "poor." Consistency in image quality assessment between observers was determined using the kappa test and intraclass correlation coefficient (ICC). The chi-square test, Student's t-test, and the Mann-Whitney U test were employed to compare the categorical and continuous variables between the RBT and FT groups. Results: The observers showed a high level of agreement in image quality assessment. There were no significant differences in ITR, SITR, acquisition time, SNR, and CNR between the RBT and FT groups (all p > 0.05). Both subjective and objective evaluations indicated no notable difference in image quality between the two groups (p > 0.05). Conclusion: The FMCW trigger technique is a viable alternative to the traditional respiratory belt trigger in scenarios of relatively stable breathing. It offers potential benefits, such as reducing operational demands on technicians and improving MRI workflow efficiency. [ABSTRACT FROM AUTHOR]