Image super-resolution (SR) is a significant technique in image processing as it enhances the spatial resolution of images, enabling various downstream applications. Based on recent achievements in SR studies in computer vision, deep-learning-based SR methods have been widely investigated for remote sensing images. In this study, we proposed a two-stage approach called bicubic-downsampled low-resolution (LR) image-guided generative adversarial network (BLG-GAN) for remote sensing image super-resolution. The proposed BLG-GAN method divides the image super-resolution procedure into two stages: LR image transfer and super-resolution. In the LR image transfer stage, real-world LR images are restored to less blurry and noisy bicubic-like LR images using guidance from synthetic LR images obtained through bicubic downsampling. Subsequently, the generated bicubic-like LR images are used as inputs to the SR network, which learns the mapping between the bicubic-like LR image and the corresponding high-resolution (HR) image. By approaching the SR problem as finding optimal solutions for subproblems, the BLG-GAN achieves superior results compared to state-of-the-art models, even with a smaller overall capacity of the SR network. As the BLG-GAN utilizes a synthetic LR image as a bridge between real-world LR and HR images, the proposed method shows improved image quality compared to the SR models trained to learn the direct mapping from a real-world LR image to an HR image. Experimental results on HR satellite image datasets demonstrate the effectiveness of the proposed method in improving perceptual quality and preserving image fidelity.

The application of remote sensing techniques for disaster management often requires rapid damage assessment to support decision-making for post-treatment activities. As the on-demand acquisition of pre-event very high-resolution (VHR) images is typically limited, PlanetScope (PS) offers daily images of global coverage, thereby providing favorable opportunities to obtain high-resolution pre-event images. In this study, we propose an unsupervised change detection framework that uses post-fire VHR images with pre-fire PS data to facilitate the assessment of wildfire damage. To minimize the time and cost of human intervention, the entire process was executed in an unsupervised manner from image selection to change detection. First, to select clear pre-fire PS images, a blur kernel was adopted for the blind and automatic evaluation of local image quality. Subsequently, pseudo-training data were automatically generated from contextual features regardless of the statistical distribution of the data, whereas spectral and textural features were employed in the change detection procedure to fully exploit the properties of different features. The proposed method was validated in a case study of the 2019 Gangwon wildfire in South Korea, using post-fire GeoEye-1 (GE-1) and pre-fire PS images. The experimental results verified the effectiveness of the proposed change detection method, achieving an overall accuracy of over 99% with low false alarm rate (FAR), which is comparable to the accuracy level of the supervised approach. The proposed unsupervised framework accomplished efficient wildfire damage assessment without any prior information by utilizing the multiple features from multi-sensor bi-temporal images.

Stereo matching algorithms are considered one of the most important sub-tasks in 3D reconstruction, as 3D coordinates are derived from the disparity values of pixels obtained through stereo matching. Recently, deep learning-based satellite stereo matching algorithms have been widely investigated, as they can capture both deep and shallow features of complex satellite scenes. However, several problems in satellite stereo matching, due to the unique properties of satellite images, remain unsolved, particularly in textureless and repetitive regions. In these regions, a single object in a satellite image is likely to be matched with similar objects, causing multiple disparity probabilities and shifts in the disparity estimation. To address the problem of disparity shifts, we propose a novel cost volume refinement strategy (CVRS). CVRS introduces both left-right and left-left cost volumes, which work together to refine disparities and eliminate false matches in textureless or repetitive regions, while preserving the original disparity values. With CVRS, we propose a new model for satellite stereo matching, the self-refined cost volume network (SRCV-Net). We evaluated CVRS and SRCV-Net on the US3D and WHU-Stereo datasets, comparing it using the EPE and D1 metrics. The application of CVRS demonstrated performance improvements in all models, and SRCV-Net achieved superior accuracy in satellite stereo matching. Furthermore, CVRS can be easily applied to various models with minimal structural changes and a small increase in parameters. SRCV-Net, with its innovative CVRS, provides an effective solution to the challenges of satellite stereo matching, offering enhanced accuracy, efficiency, and adaptability.

Image super-resolution (SR) aims to enhance the spatial resolution of images and overcome the hardware limitations of imaging systems. While deep learning networks have significantly improved SR performance, obtaining paired low-resolution (LR) and high-resolution (HR) images for supervised learning remains challenging in real-world scenarios. In this paper, we propose a novel unsupervised image super-resolution model for real-world remote sensing images, specifically focusing on HR satellite imagery. Our model, the bicubic-downsampled LR image-guided generative adversarial network for unsupervised learning (BLG-GAN-U), divides the SR process into two stages: LR image domain translation and image super-resolution. To implement this division, the model integrates omnidirectional real-to-synthetic domain translation with training strategies such as frequency separation and guided filtering. The model was evaluated through comparative analyses and ablation studies using real-world LR-HR datasets from WorldView-3 HR satellite imagery. The experimental results demonstrate that BLG-GAN-U effectively generates high-quality SR images with excellent perceptual quality and reasonable image fidelity, even with a relatively smaller network capacity.

Vegetation segmentation in Very High-Resolution (VHR) satellite imagery has become an essential task for ecological monitoring, supporting diverse applications such as large-scale vegetation conservation and detailed segmentation of small local areas. In particular, multi-class vegetation segmentation, which distinguishes various vegetation types beyond simple binary segmentation of vegetation and non-vegetation, enables detailed analysis of subtle ecosystem changes and has gained increasing importance. However, the annotation of VHR satellite imagery requires extensive time and effort, resulting in a lack of datasets for vegetation segmentation, especially those including multi-class annotations. To address this limitation, this study proposes MultiVeg, a deep learning dataset based on VHR satellite imagery for detailed multi-class vegetation segmentation. MultiVeg includes preprocessed 0.5 m resolution images collected by the KOMPSAT-3 and 3A satellites from 2014 to 2023, covering diverse environments such as urban, agricultural, and forest regions. Each image was carefully annotated by experts into three semantic classes, which are Background, Tree, and Low Vegetation, and validated through a structured quality check process. To verify the effectiveness of MultiVeg, seven representative semantic segmentation models, including convolutional neural network and Transformer-based architectures, were trained and comparatively analyzed. The results demonstrated consistent segmentation performance across all classes, confirming that MultiVeg is a high-quality and reliable dataset for deep learning-based multi-class vegetation segmentation research using VHR satellite imagery. The MultiVeg will be publicly available through GitHub (release v1.0), serving as a valuable resource for advancing deep leaning-based vegetation segmentation research in the remote sensing field.

Image super-resolution (SR) techniques have achieved significant performance improvements with the advancement of deep learning. Accordingly, deep learning-based SR methods have become the mainstream approach in SR research and are widely applied across various fields, including remote sensing. However, most state-of-the-art SR studies are primarily driven by computer vision research and tend to focus on generating visually realistic images rather than preserving structural fidelity with respect to the input images. In remote sensing applications, maintaining structural fidelity is particularly important because SR outputs are often used in downstream analytical tasks such as object detection.In this study, we investigate the use of edge loss to enhance the structural fidelity of SR images for remote sensing imagery. The effectiveness of edge loss was evaluated using multiple benchmark datasets on both convolutional neural network (CNN)- and generative adversarial network (GAN)-based SR models. Several representative SR network architectures and GAN training frameworks were employed to assess the impact of integrating edge loss into the training objective. The experimental results demonstrate that incorporating edge loss improves both the structural fidelity and perceptual quality of SR images. Among the evaluated edge operators, the Prewitt-based edge loss showed the most consistent improvements compared with the Sobel- and Laplacian-based edge losses. These results indicate that edge loss is an effective and easily implementable strategy for improving SR reconstruction quality in remote sensing imagery. Furthermore, it can be combined with other edge-aware techniques to further enhance perceptual quality.