Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of hyperspectral image (HSI) classification. Moreover, due to the high performance demonstrated by the transformer architecture in classification tasks, there has been a proliferation of neural networks combining CNNs and transformers for HSI classification. However, the majority of the current methods focus on extracting spatial–spectral features from the HSI data of a single size for a pixel, overlooking the rich multi-scale feature information inherent to the data. To address this problem, we designed a novel transformer network with a CNN-enhanced cross-attention (TNCCA) mechanism for HSI classification. It is a dual-branch network that utilizes different scales of HSI input data to extract shallow spatial–spectral features using a multi-scale 3D and 2D hybrid convolutional neural network. After converting the feature maps into tokens, a series of 2D convolutions and dilated convolutions are employed to generate two sets of Q (queries), K (keys), and V (values) at different scales in a cross-attention module. This transformer with CNN-enhanced cross-attention explores multi-scale CNN-enhanced features and fuses them from both branches. Experimental evaluations conducted on three widely used hyperspectral image (HSI) datasets, under the constraint of limited sample size, demonstrate excellent classification performance of the proposed network.

The applications of machine learning/deep learning (ML/DL) methods in meteorology have developed considerably in recent years. Massive amounts of meteorological data are conducive to improving the training effect and model performance of ML/DL, but the establishment of training datasets is often time consuming, especially in the context of supervised learning. In this paper, to identify the two-dimensional (2D) structures of extratropical cyclones in the Northern Hemisphere, a quasi-supervised reidentification method for extratropical cyclones is proposed. This method first uses a traditional automatic cyclone identification method to construct a trainable labeled dataset and then reidentifies extratropical cyclones in a quasi-supervised fashion by using a (pre-trained) Mask region-based convolutional neural network (Mask R-CNN) model. In comparison, the new method increases the number of identified cyclones by 8.29%, effectively supplementing the traditional method. The newly recognized cyclones are mainly shallow or moderately deep subsynoptic-scale cyclones. However, a considerable portion of the new cyclones along the coastlines of the oceans are accompanied by strong winds. In addition, the Mask R-CNN model also shows good performance in identifying the horizontal structures of tropical cyclones. The quasi-supervised concept proposed in this paper may shed some light on accurate target identification in other research fields.