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Single Image Super-Resolution (SISR) for image enhancement enables the generation of high spatial resolution in Remote Sensing (RS) images without incurring additional costs. This approach offers a practical solution to obtain high-resolution RS images, addressing challenges posed by the expense of acquisition equipment and unpredictable weather conditions. To address the over-smoothing of the previous SISR models, the diffusion model has been incorporated into RS SISR to generate Super-Resolution (SR) images with enhanced textural details. In this paper, we propose a Diffusion model with Adversarial Learning Strategy (DiffALS) to refine the generative capability of the diffusion model. DiffALS integrates an additional Noise Discriminator (ND) into the training process, employing an adversarial learning strategy on the data distribution learning. This ND guides noise prediction by considering the general correspondence between the noisy image in each step, thereby enhancing the diversity of generated data and the detailed texture prediction of the diffusion model. Furthermore, considering that the diffusion model may exhibit suboptimal performance on traditional pixel-level metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), we showcase the effectiveness of DiffALS through downstream semantic segmentation applications. Extensive experiments demonstrate that the proposed model achieves remarkable accuracy and notable visual enhancements. Compared to other state-of-the-art methods, our model establishes an improvement of 189 for Fréchet Inception Distance (FID) and 0.002 for Learned Perceptual Image Patch Similarity (LPIPS) in a SR dataset, namely Alsat, and achieves improvements of 0.4%, 0.3%, and 0.2% for F1 score, MIoU, and Accuracy, respectively, in a segmentation dataset, namely Vaihingen. [ABSTRACT FROM AUTHOR]

Landslide mapping via pixel-wise classification of remote sensing imagery is essential for hazard prevention and risk assessment. Deep-learning-based change detection greatly aids landslide mapping by identifying the down-slope movement of soil, rock and other materials from bitemporal images, benefiting from the feature representation capabilities of convolutional neural networks. However, these networks rely on large amounts of pixel-level annotated data to achieve their promising performance and they normally exhibit weak generalization capability on heterogeneous image data from unseen domains. To address these issues, we propose a prototype-guided domain-aware progressive representation learning (PG-DPRL) method for cross-domain landslide mapping from large-scale remote sensing images based on the multitarget domain adaptation (MTDA) technique. PG-DPRL attempts to learn a shared landslide mapping network that performs well in multiple target domains with no additional effort for sample annotation. Specifically, PG-DPRL adopts a near-to-far adaptation strategy to gradually align the representation distributions of all target domains with the source domain, considering discrepancies between them. On this basis, cross-domain prototype learning is exploited to generate reliable domain-specific pseudo-labels and aggregate representations across domains to learn a shared decision boundary. In each DPRL step, the prototype-guided adversarial learning (PGAL) algorithm is performed to achieve category-wise representation alignment and improve the discriminative capability of representations by introducing the Wasserstein distance metric and cross-domain prototype consistency (CPC) loss. Experiments on a global very-high-resolution landslide mapping (GVLM) dataset consisting of 17 heterogeneous domains from different landslide sites demonstrate the effectiveness and robustness of PG-DPRL. It considerably improves the transferability of landslide mapping networks and outperforms several state-of-the-art approaches in terms of total and average accuracy metrics among all target domains. [ABSTRACT FROM AUTHOR]