Polarization imaging beyond traditional imaging offers a wide range of advantages, encompassing not only the detection of geometric shapes and surfaces, but also the measurement of physical properties. The divided focal plane (DoFP), an ideal real-time imaging, comprises a 2 × 2 linear polarization filter overlaid on a focal plane array sensor. Consequently, performing polarization demosaicing (PDM) becomes crucial to restore the missing components in the pixel data. The objective of PDM extends beyond the acquisition of intensity, and its primary goal is to minimize the estimation error of polarization characteristics. However, existing demosaicing networks often neglect the issues of polarization spatial-intensity attention and are trained similarly for generalization, thereby failing to effectively distinguish between genuine details and artifacts, which renders demosaicing vulnerable to distortion. In this work, we propose a novel generative adversarial network framework that integrates attention mechanisms and progressive discrimination for polarimetric images. The GAN-based framework introduces a global attention mechanism that enhances the interaction between spatial and polarization intensity information across different regions, resulting in improved network performance. Additionally, the framework generates artifact maps to normalize the model training process. To evaluate the effectiveness of our method, we assess its performance using full-resolution polarimetric image and compare it against traditional and recent PDM methods. The experimental results demonstrate that our proposed method achieves the highest peak signal-to-noise ratio (PSNR) and exhibits excellent visual quality for the output Stokes images when compared to existing methods.

During satellite remote sensing imaging, the use of Bayer mode sensors holds significant importance in saving airborne computing resources and reducing the burden of satellite transmission systems. The demosaicing techniques play a key role in this process. The integration of Generative Adversarial Networks (GANs) has garnered significant interest in the realm of image demosaicking, owing to their ability to generate intricate details. However, when demosaicing mosaic images in remote sensing techniques, GANs, although capable of generating rich details, often introduce unpleasant artifacts while generating content. To address this challenge and differentiate between undesirable artifacts and realistic details, we have devised a novel framework based on a Progressive Discrimination Strategy within a Generative Adversarial Network architecture for image demosaicking. Our approach incorporates an artifact-weighted Location Map refinement technique to guide the optimization process towards generating authentic details in a stable and precise manner. Furthermore, our framework integrates a global attention mechanism to boost the interaction of spatial-channel information across different dimensions, thereby enhancing the performance of the generator network. Moreover, we conduct a comparative analysis of various prevalent attention mechanisms in the context of remote sensing image demosaicking. The experimental findings unequivocally demonstrate that our proposed methodology not only achieves superior reconstruction accuracy on the dataset but also enhances the perceptual quality of the generated images. By effectively mitigating artifacts and emphasizing the generation of true details, our approach represents a significant advancement in the field of remote sensing image demosaicking, promising enhanced visual fidelity and realism in reconstructed images.

This article presents the scientific results of the 2025 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee, the University of Tokyo, RIKEN, and ETH Zurich. The focus of the contest was to develop innovative solutions for all-weather land-cover and building damage mapping using multimodal SAR and optical EO data at submeter resolution. The contest is organized into two distinct tracks. Track 1 focuses on land-cover mapping and Track 2 is about building damage mapping. The competition presented two primary technical challenges: the effective integration of multimodal data and the development of robust models capable of handling noisy labels. The contest saw significant global engagement, with Track 1 receiving 507 registrations and 3,859 successful submissions, while Track 2 followed a similar trend with 423 registered teams and 5,008 successful entries. This paper provides the methodologies and results achieved by the first- and secondranked teams from each track. To promote transparency and ensure the reproducibility of results, all participating teams in this year's contest have publicly released their code. Furthermore, the dataset used in this competition has been made publicly available to the community to support and encourage further research in the field.