In recent years, deep learning has witnessed astonishing success in the field of remote sensing in images. Generally, deep learning requires a large amount of labeled training data. Nevertheless, in remote sensing, sufficient labeled data are scarce because labeled data are often difficult, expensive, or time-consuming to obtain. To address these problems, we propose a deep curriculum learning semi-supervised framework (DCLSSF) for remote sensing image scene classification. This framework employs a multimodal deep curriculum learning method which can realize the classification of images on a range of easy–difficult. Specifically, by utilizing multiple pretrained networks to extract multiple deep features of images as their multimodal feature representations, it can comprehensively mine the information from labeled and unlabeled images from diverse perspectives. Subsequently, a feature fusion method is used on deep features of different modalities to obtain deep fusion features with a strong discrimination ability and low dimensionality. Finally, the multimodal deep features are fed into multimodal curriculum learning methods for classification. Multimodal curriculum learning can integrate the easy curricula recommended by each modal according to the order of the samples of each modal and then learn step by step. Experiments on three publicly available datasets (UC Merced, AID, and NWPU-RESISC45) show that the semi-supervised classification framework achieves high accuracy rates (99.14%, 97.95%, and 93.01%), even surpassing those of the most supervised classification methods. The DCLSSF method can not only fully exploit the rich features extracted by the multimodal deep learning network but can also perform the semi-supervised classification of unlabeled samples in a range of easy–difficult.

The Transformer architecture has gained widespread acceptance in image segmentation. However, it sacrifices local feature details and necessitates extensive data for training, posing challenges to its integration into computer-aided medical image segmentation. To address the above challenges, we introduce CCFNet, a collaborative cross-fusion network, which continuously fuses a CNN and Transformer interactively to exploit context dependencies. In particular, when integrating CNN features into Transformer, the correlations between local and global tokens are adaptively fused through collaborative self-attention fusion to minimize the semantic disparity between these two types of features. When integrating Transformer features into the CNN, it uses the spatial feature injector to reduce the spatial information gap between features due to the asymmetry of the extracted features. In addition, CCFNet implements the parallel operation of Transformer and the CNN and independently encodes hierarchical global and local representations when effectively aggregating different features, which can preserve global representations and local features. The experimental findings from two public medical image segmentation datasets reveal that our approach exhibits competitive performance in comparison to current state-of-the-art methods.