Even with an unprecedented breakthrough of deep learning in electroencephalography (EEG), collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling. Recent study proposed to solve the limited label problem via domain adaptation methods. However, they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries, which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely. A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study. The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers' outputs. Besides, a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed. Finally, a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network (CNN) is constructed. Extensive experiments on SEED and SEED‐IV are conducted. The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

With the development of supervised deep neural networks, classification performance on existing remote sensing scene datasets has been markedly improved. However, supervised learning methods rely heavily on large-scale tagged examples to obtain a better prediction performance. The lack of large-scale tagged remote sensing scene images has become the primary bottleneck in scene classification. To deal with this issue, a novel scene classification method using self-supervised gated self-attention generative adversarial networks (GANs) with similarity loss is proposed. Specifically, the gated self-attention module is first introduced into GANs to focus on key scene areas and filter useless information for strengthening feature representations. Then, the pyramidal convolution block is introduced into the residual block of the discriminator to capture different levels of details in the image using different types of filters with varying sizes and depths for enhancing the feature representations of the discriminator. Additionally, a novel similarity loss item is integrated into the discriminator to leverage self-supervised learning. Besides, spectral normalization is introduced into both the generative network and discriminative network to stabilize training and enhance feature representations. The architecture of multilevel feature fusion is integrated into the discriminative network to achieve more discriminant representation. Experimental results on the AID and NWPU-RESISC45 datasets show that the proposed method achieves the best performance compared to the existing unsupervised classification methods.

Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly contain various types of ground objects and complex backgrounds, most methods cannot focus on saliency features of scene, which limits the classification performances. To address this issue, we propose a novel Saliency Dual Attention Residual Network (SDAResNet) to extract both cross-channel and spatial saliency information for scene classification of RSI. More specifically, the proposed SDAResNet consists of spatial attention and channel attention, in which spatial attention is embedded into low-level feature to emphasize saliency location information and suppress background information, while channel attention is integrated into high-level features to extract saliency meaningful information. Additionally, several image classification tricks are used to further improve classification accuracy. Finally, extensive experiments on two challenging benchmark RSI datasets are presented to demonstrate that our methods outperform most state-of-the-art approaches significantly.

With the advent of a large number of remote sensing images (RSIs), scene classification of RSI is widely applied to many fields such as urban planning, natural disaster detection, and environmental monitoring. Compared with the natural image field, the lack of labeled RSI is a bottleneck of supervised scene classification methods based on deep learning. Meanwhile, unsupervised scene classification is difficult to meet actual needs. To this end, we propose a novel semisupervised scene classification method for RSI using generative adversarial nets (GANs), in which a gating unit, a self-attention gating (SAG) module, and a pretrained Inception V3 branch are introduced into discriminative network to enhance the feature representation capability for facilitating semisupervised classification. To be specific, the gating unit aims to learn the weights of each feature map and capture the dependence relationship between features. The SAG module aims to capture a long-range dependence for adaptively focusing on important scene regions. The Inception V3 branch aims to extract the high-level semantic representation of input images and further enhance the discriminant ability by gating unit and SAG module. Furthermore, a new optimization term is incorporated into the generator loss to indirectly drive discriminator to correctly classify scene images. To verify the effectiveness of the proposed method, extensive experimental results on UC Merced and EuroSAT data sets demonstrate that the method surpasses most of the state-of-the-art semisupervised image classification methods significantly, especially when only few samples are tagged.

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of $$Q_{EN}$$ , $$Q_{NIQE}$$ , $$Q_{SD}$$ , $$Q_{SSEQ}$$ and $$Q_{TMQI}$$ .