To address the problems of high dimensionality of hyperspectral images, small training samples and overfitting and too many parameters caused by model training, a hyperspectral image classification model (CBAM-HybridSN) with improved hybrid neural network combined with convolutional attention mechanism is proposed. The model firstly uses principal component analysis to remove the redundancy of spectral dimensional data, extracts the null spectral features by the hybrid neural network model, and introduces the convolutional attention module to rescale the extracted features and highlight the important features, thus improving the classification accuracy. In the experiments, the Pavia University dataset was divided into samples with 1:9, and the OA reached 99.32%, achieving accurate classification of hyperspectral images with small samples.
In recent years, with the rapid development of computers, the Internet and other modern technologies, the information age has quietly arrived. Libraries, as an important way to learn knowledge, have thus ushered in great development. In response to the problem of traditional libraries' static web page compatibility, this paper designs how a responsive library platform based on SSM framework and Bootstrap can be deployed under Centos Linux platform, tested and packaged through Docker technology. Docker covers all stages of platform development and operation and maintenance, and with the help of Docker technology, it can be built once and Docker covers all the phases of platform development and operation and maintenance, and it can be built once and run everywhere with Docker technology. When using LVS to deploy Web server clusters, Docker images can be used to deploy Web servers directly. The test results show that the platform has strong portability, saves the development and deployment costs, and has great advantages in the rapid deployment of large-scale applications.
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