The joint semantic segmentation of multi-modal remote sensing images can make up for the problem of insufficient features of single-modal images and effectively improve the classification accuracy. Some deep learning methods have achieved good performance, but they face problems such as complex network structure, large number of parameters, and difficulty in deployment. In this paper, more attention is paid to front-end and branch-level feature transformation to obtain multi-scale semantic information. The multi-scale dilated extraction module (MDEM) is constructed to mine the specific features of different modalities. The multi-modal complementary attention module (MCAM) is designed for further acquiring prominent complementary content. The concatenated features are transmitted and reused by the dense convolution to complete the encoding. Ultimately, a general and concise end-to-end model is proposed. Comparative experiments are carried out on three heterogeneous datasets, and the model put forward performs well in qualitative analysis, quantitative comparison and visual effect. Meanwhile, the dexterity and practicability of the model are more prominent, which can provide support for lightweight design and hardware deployment.

Object detection plays a crucial role in remote sensing due to the urgent demands of various applications, such as urban planning and environmental monitoring. Despite notable progress, current methods still struggle with detecting challenging small objects. At the object level, the limited pixel representation, blurred details, and background interference of small objects impose greater demands on feature extractors. At the network level, resource bias fails to provide adequate learning signals for these objects. In this paper, we propose a Sparse Transformer-based detector (STDet) to tackle these challenges. Specifically, we design a Local-to-Global Transformer network (LGFormer) to explore essential feature representations. The Local Transformer Block establishes correlations between tokens and their surrounding data, while the Global Transformer Block captures long-distance dependencies related to the objects. Meanwhile, we introduce a Scale-Balanced Label Assignment (SBLA) strategy that considers more samples to small objects. SBLA dynamically shifts the learning focus to easily overlooked objects and alleviates the issue of sample imbalance. Extensive experiments on three large-scale remote sensing datasets demonstrate the effectiveness of STDet and its superiority in small object detection.

Graph convolutional networks (GCNs) can extract features of samples in non-Euclidean space, which can be used for hyperspectral image (HSI) classification in collaboration with convolutional neural networks (CNNs). The features of GCNs and CNNs are incompatible to a certain extent, and traditional graph convolution methods use a single channel two-dimensional matrix to extract features. As a result, it is difficult to explore the relationships fully and flexibly between samples. To further exploit the potential of these two networks for collaborative extraction of HSI features, we propose a fusion framework based on multichannel GCNs and class-guided attention mechanism (MG2A). Specifically, a multichannel graph convolutional network (MGCN) module is designed for batchwise network training, where the adjacent matrix of each channel contains different information between samples. In addition, we develop a class-guided attention mechanism to adaptively fuse the features of multiple MGCN modules and learn the transformation process of the features. Finally, the features of CNNs and MGCN modules are fused at multiple layers through a fusion framework. Experimental results on four benchmark HSI datasets show that MG2A achieves better classification performance compared to other state-of-the-art methods.

In remote sensing, it is desirable to improve image resolution by using the image super-resolution (SR) technique. However, there are two challenges: the first one is that high-resolution (HR) images are insufficient or unavailable; another one is that the single degradation model such as bicubic (BIC) cannot super-resolve favorable images in the real world. To address the above two problems, this article presents a multi-degradation, unsupervised SR method based on deep learning. This framework consists of a degrader$ {D}$to fit the image degradation model and a generator$ {G}$to generate SR image. By introducing$ {D}$, calculating the loss function between SR image and HR image as supervised SR methods did can be converted into calculating loss between low resolution (LR) image and image degraded by SR image, thereby realizing unsupervised learning. Experiments on several degradation models show that our method renders the state-of-the-art results compared with existing unsupervised SR methods, and achieves competitive results in contrast with supervised SR methods. Moreover, for real remote sensing images obtained by the Jilin-1 satellite, our method obtained more plausible results visually, which demonstrate the potential in real-world applications.

The development of deep learning has brought new prospects into the field of hyperspectral classification, and the classification ability of this method for the classification of hyperspectral images (HSIs) has been continuously improved. However, there are still some problems that must be solved; for example, the spectral–spatial features of HSIs are not effectively extracted, the labeled samples in the data set are limited, and the number of samples in different categories is imbalanced. To facilitate the progress of hyperspectral classification, a spectral–spatial fractal residual convolutional neural network with data balance augmentation is proposed here. In this network, a data balance augmentation approach that can solve the problems of limited labeled data and imbalanced categories is proposed. In addition, the spectral–spatial residual module is proposed to learn the spectral–spatial information and alleviate the problem of model degradation effectively. In addition, the spectral–spatial focal structure, which can guarantee the integrity of the information, is introduced. Moreover, the spectral–spatial dimensional transformation module, which can reduce the size and number of hyperspectral feature maps without losing the fine features, is presented. In particular, the proposed network has a strong ability to classify the categories that have a small number of samples and reaches the state-of-the-art level for three benchmark data sets.