Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B3-2026, pp. 49-55· 0 citations· 9 references
TL;DR
Investigation of the use of edge loss to enhance the structural fidelity of SR images for remote sensing imagery indicates that edge loss is an effective and easily implementable strategy for improving SR reconstruction quality in remote sensing imagery.
Abstract
Abstract. 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.
This work proposes a multistep tunable SR network named MTSR, which simulates the mapping process from low-resolution inputs to high-resolution outputs and introduces adjustable parameters, and enables the method to produce nonsmooth SR results without artifacts while preserving rich and realistic textural details.
Guodong Ding, Jinhe Hu, Wei Xue et al.· IEEE Journal of Selected Top...· 0 citations
A novel SR framework based on the flow matching paradigm and a diffusion transformer, named FlowT-SR, which achieves superior and reliable reconstruction quality by jointly mitigating sensor noise and thin cloud interference, achieving superior reconstruction performance compared with current state-of-the-art methods in terms of both PSNR and SSIM.
Yu-Tong Zhang, Guang Yang, Rong Liu et al.· Italian National Conference...· 0 citations
Satellite imagery often suffers from limited spatial resolution and, in many cases, high acquisition costs. These factors restrict their use in applications such as urban monitoring, land management, and wildlife studies. This work proposes an AI-based super-resolution approach that leverages high resolution aerial imagery to train a Generative Adversarial Network. Specifically, the ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) architecture is adapted and trained using aerial orthophotos, enabling the transfer of learned spatial representations to low-resolution satellite images. The trained model is evaluated on satellite image patches at 2 and 4 super-resolution scales. Performance is assessed using structural, perceptual, and chromatic metrics, including SSIMY, MS-SSIM, LPIPS and CIEDE2000. The results show clear improvements, with increased sharpness, enhanced edge definition, and consistent reconstruction of urban structures and terrain features. From a quantitative perspective, the 2 scale achieves the best overall metric values, while the 4 scale maintains stable and meaningful performance despite the higher reconstruction difficulty. These findings demonstrate the feasibility of transferring super-resolution capabilities from aerial images to satellite imagery, even in the presence of spectral and geometric differences between acquisition domains. Overall, this study provides a solid foundation for the development of low-cost, AI-driven satellite image super-resolution models and outlines future research directions focused on dataset expansion, domain adaptation strategies, and sensor-specific architectural improvements.
Magda Alexandra Trujillo-Jiménez, Francisco Iaconis, Debora Pollicelli et al.· IEEE Latin America Transacti...· 0 citations
This paper develops a dual-path generator architecture: the standard path thoroughly extracts deep high-level features, while the auxiliary path preserves prior information and high-frequency details, and introduces a multi-scale feature extraction module to boost the model’s ability to capture features at various scales.
MAPSRNet offers a practical solution for scenarios where HR imagery is limited or unavailable, highlighting its potential for large-scale remote sensing applications and demonstrating that perceptual and structural fidelity, rather than pixel-level similarity, can drive superior performance in urban land cover segmentation.
Yuwei Cai, Zhimeng He, Meiliu Wu et al.· ISPRS Annals of the Photogra...· 0 citations
Remote sensing images acquired under hazy atmospheric conditions often exhibit reduced contrast, color distortion, and loss of fine structures, which adversely affect visual interpretation and subsequent tasks such as land cover classification and object detection. Existing dehazing approaches either rely on computationally intensive transformer architectures or convolutional models with limited global modeling capacity, making it difficult to balance performance and efficiency for high-resolution remote sensing imagery. We propose a Lightweight Multiscale Bidirectional Network (LMBNet) for remote sensing image dehazing. The framework adopts a hierarchical encoder–decoder architecture to capture degradation patterns across different spatial resolutions. A Sparsity-Compensated Dual-stream Transformer (SCDT) block is introduced as the core feature extractor. The Sparsity-Compensated Self-Attention (SCSA) mechanism employs rank-based masking to suppress irrelevant responses and enhance discriminative nonlocal aggregation. In parallel, a Dual-Stream Feed-forward Network (DSFN) leverages multiscale depth-wise convolutions and differential modulation to refine spatial details and reduce redundancy. A composite loss integrating Charbonnier, edge, and frequency terms further improves structural fidelity. Experimental results on benchmark datasets including StateHaze1k and RICE demonstrate that the proposed method achieves competitive performance with only 14.31 M parameters, yielding up to 29.58 dB in peak signal-to-noise ratio (PSNR) and 0.9411 in Structural Similarity Index (SSIM), indicating its suitability for practical remote sensing applications
Unknown authors· Journal of Applied Remote Se...· 0 citations
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