In recent years, the rapid advancement of multi-sensory platforms has significantly increased the availability of multisource remote sensing data, facilitating its systematic application to various tasks. The joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data remains a critical research topic, with a key challenge being the effective extraction and integration of complementary information from multi-source remote sensing data. However, existing graph convolutional networks (GCNs)-based methods often fail to account for the heterogeneous topological relationships between HSI and LiDAR. Moreover, the discriminative power of HSI and LiDAR features extracted by existing methods is insufficient. In addition, existing methods are unable to fully exploit the rich self-supervised information present in local neighborhood. To address these limitations, we propose a heterogeneous contrastive graph fusion network (HCGFN) for the joint classification of HSI and LiDAR data. First, we propose a branch enhancement module to enhance the discriminative power of HSI and LiDAR. Second, a contrastive learning module is introduced to effectively align HSI and LiDAR representations. Finally, we propose a dynamic heterogeneous graph structure learning module to model heterogeneous relationship and achieve efficient interaction and effective fusion between HSI and LiDAR. The extensive experimental results on three benchmark datasets indicate the effectiveness of the proposed HCGFN compared with other state-of-the-art methods. Specifically, under limited training samples, the proposed HCGFN outperformed state-of-the-art methods in overall accuracy by 5.10%, 2.46%, and 8.79% on datasets Trento, MUUFL, and Houston2013, respectively.

Graph convolutional networks (GCNs) have recently gained prominence in hyperspectral images (HSIs) classification tasks given their superior performance on non-Euclidean data. However, GCN-based methods are heavily reliant on complete graph structural information, which can cause the aggregation and transmission of information across nodes from differing classes, thereby compromising the classification performance. Furthermore, the scarcity of labeled pixels in HSIs often limits the representational capability of such methods. To address these issues, we propose an aggregative and contrastive dual-view graph attention network (ACoD-GAT) for HSI classification. Specifically, we present a progressive aggregation module, including a pixel clustering submodule and a node aggregation submodule to exploit semantic information at various levels. Besides, we integrate multiscale manipulation with a diffusion matrix to construct the dual view to further extract semantic information from both local and global perspectives. Moreover, we design an unsupervised contrastive loss function and a supervised contrastive loss function to facilitate contrastive learning on the dual view, improving the representational capabilities of ACoD-GAT with very few labeled samples. The extensive experimental results on four benchmark datasets demonstrate the superiority of the proposed ACoD-GAT compared with other state-of-the-art methods.

Conventional deep learning-based methods for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution (SR) outputs of these methods are yet to become sufficiently satisfactory in visual quality. Recent diffusion model-based generative deep learning models are capable to enhance the visual quality of output images, but this capability is limited due to their sampling efficiency. In this article, we propose FastDiffSR, an SRSISR method based on a conditional diffusion model. Specifically, we devise a novel sampling strategy to reduce the number of sampling steps required by the diffusion model while ensuring the sampling quality. Meanwhile, the residual image is adopted to reduce computational costs, demonstrating that integrating channel attention and spatial attention begets a further improvement in the visual quality of output images. Compared to the state-of-the-art (SOTA) convolutional neural network (CNN)-based, GAN-based, and Transformer-based SR methods, our FastDiffSR improves the learned perceptual image patch similarity (LPIPS) by 0.1–0.2 and achieves better visual results in some real-world scenes. Compared with existing diffusion-based SR methods, our FastDiffSR achieves significant improvements in pixel-level evaluation metric peak signal-noise ratio (PSNR) while having smaller model parameters and obtaining better SR results on Vaihingen data with faster inference time by 2.8–28 times, showing excellent generalization ability and time efficiency. Our code will be open source athttps://github.com/Meng-333/FastDiffSR.

Super-resolution (SR) methods have significantly contributed to the improvement of the spatial resolution of remote sensing (RS) images. The development of deep learning empowers novel methods to learn informative feature representation from massive low-resolution (LR) and high-resolution (HR) image pairs. Conventional RS image SR methods, however, may fail in large-scale ($\times 8$and$\times 9$) SR tasks. Specifically, a larger scale factor corresponds to less information in LR images, which is a considerable challenge to SR. To address the issue, we propose a novel method for single RS image SR (SRSISR) based on stratified dense sampling to effectively extract image features. Specifically, the proposed SR dense-sampling residual attention network (SRDSRAN) combines dense sampling and residual learning to improve multilevel feature fusion and gradient propagation and employs local and global attentions to learn important features and long-range interdependence in the channel and spatial dimensions. Meanwhile, we also devise a discriminator model using local and global attentions and with the loss function integrating${L}_{1}$pixel loss,${L}_{1}$perceptual loss, and relativistic adversarial loss to obtain the perceptually realistic images. Besides, we introduce a chain training to promote performance and expedite the training process for large-scale SR. Experimental results on UC Merced image and other multispectral data demonstrated that our SRDSRAN outperformed the current state-of-the-art methods quantitatively and in visual quality and obtained a higher classification accuracy in scene classification, proving its potential for applications with other downstream tasks. The code of SRADSGAN will be available athttps://github.com/Meng-333/SRADSGAN.

Downscaling nighttime light (NTL) from satellite imagery presents valuable applications at a more detailed spatial scale, especially in the realms of urban expansion and socio-economic assessment. Nevertheless, due to the complexity of geographical conditions and uncertainties in the relationships among multiple factors, the precision of NTL downscaling often encounters constraints. In this work, an incorporated multifactor geographically neural network weighted regression (MF-GNNWR) NTL downscaling framework is proposed to solve the spatial nonstationarity in high-heterogeneous urban areas, which mainly uses geographically neural network weighted regression (GNNWR) combined with multiple factors including surface physical characteristics, socio-economic attributes, and human activities to improve the accuracy of NTL, particularly in urban regions with complicated land cover. The findings illustrate that the MF-GNNWR framework displays finer downscaling accuracy on different land cover, effectively enhancing data quality. Notably, our findings underscore the pronounced influence of socio-economic and human activity factors on NTL downscaling. Comparative analysis against several alternative downscaling methodologies reveals that the MF-GNNWR framework outperforms them, exhibiting a remarkable 23.10% improvement in the Pearson correlation coefficient (r) and achieving a root-mean-square error (RMSE) of$16.95~\text {{nW/c}{m}}^{2}{/\text {sr}}$, and after residual compensation, r continue s to increase by 1.5%, while RMSE decreases by$0.157~\text {nW/cm}^{2}{/\text {sr}}$. These findings highlight the efficacy of the proposed framework in downscaling NTL, underscoring its advantages and practical utility.