With the increasing demand for high-resolution images in remote sensing, medical imaging, and computer vision, image super-resolution has become a crucial technique for enhancing image quality. However, existing methods often face a trade-off between detail restoration and structure preservation and struggle to effectively fuse multimodal information, limiting further improvements. To address these challenges, this paper proposes DBFF-SRNet, a dual-branch frequency fusion network that separately extracts structural and texture features through dedicated encoders, and integrates them using a spatial-spectral joint fusion module along with a multi-level wavelet pyramid frequency enhancement module, enabling efficient multimodal feature fusion and detail recovery. Experimental results demonstrate that DBFF-SRNet outperforms state-of-the-art methods on benchmark datasets including Set5, Set14, BSD100, and Urban100. Specifically, on Set5, DBFF-SRNet achieves a PSNR of 32.55 dB, improving by 0.35 dB over the second-best method, with an SSIM of 0.8927 and a reduced LPIPS of 0.0574, significantly enhancing the visual quality and structural coherence of reconstructed images. The proposed model not only provides an effective solution for high-quality multimodal image reconstruction but also opens new avenues for integrating frequency-domain and spatial-domain deep learning approaches in super-resolution research.

To overcome the effect of speckle noise on SAR image change detection, a superpixel segmentation algorithm based on speckle reducing anisotropic diffusion model is proposed and applied for object-oriented SAR image change detection. Based on the traditional modeling methods of the non-similarity between pixels and seed points by combining spectral distance and spatial distance in superpixel segmentation, the concept of diffusion flux is proposed to simulate the continuous and bounded evolution of the membership of pixels and seed points in the image plane lattice. Considering the effect of speckle noise and the demand of segmenting different shape surface features in complex scenes, the speckle reducing anisotropic diffusion is used to model the diffusion flux. After superpixel segmentation of dual-temporal remote sensing images, an overlay technology is adopted to obtain the finer results. Finally, the change detection result is generated based on the superpixelized difference image by the classical fuzzy clustering algorithm FCM. The experiments carried out on Sentinel-1 SAR images by comparing algorithms fully demonstrate the effectiveness of the proposed algorithm.

This study proposed a new application paradigm of time series SAR data for sugarcane mapping. First, the LOESS smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multitemporal SAR images with high importance values. At last, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate classification map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the "salt and pepper" spots and improving the completeness of the sugarcane plots. Especially, the unreasonable non-vegetation spots in the sugarcane map were completely eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation.

This study proposes a method for the three-dimensional (3D) inversion of Interferometric Synthetic Aperture Radar (InSAR) time series measurements, focusing on land subsidence in urban contexts characterized by slow, long-term, and small deformation magnitudes. Tailored for situations primarily dependent on single-track SAR satellite data, it integrates a physical constraint model between horizontal and vertical deformation gradients. Given the localized nature of urban deformations, this method avoids using a uniform subsidence model for an entire Region of Interest (ROI). Instead, it opts for a pixel-by-pixel estimation of constraint parameters, based on a detailed analysis of subsidence in affected areas. Experiments were conducted in representative scenarios, including the subsidence observed in ocean reclaimed zones and in residential areas impacted by tunneling activities for metro line construction. The results, validated through comparison with leveling measurements, suggest that the proposed method facilitates a precise 3D inversion, capturing the complex dynamics of urban surface deformation.

Cloud detection is a crucial step in the analysis and processing of optical remote sensing satellite imagery. Existing methods often have large parameter sizes, high computational complexity, and experience a rapid drop in model accuracy when transferred to different sensors. We propose a cloud detection method based on Efficient Transformer and Neural Style Transfer, a lightweight cloud detection network that can be used across different sensors without requiring additional labeled data. Our contribution focus on two main aspects: 1.We design a lightweight cloud detection model that reduces redundant parameters without compromising model accuracy; 2.We introduce a Neural Style Transfer module for cross-sensor cloud detection, aiming to align cloud features from different sensors with training images. Experiments on the 38-Cloud dataset demonstrate that our proposed method achieves state-of-the-art performance while maintaining a small parameter size and floating-point computation. In cross-sensor cloud detection experiments, the inclusion of the Neural Style Transfer module significantly enhances the model’s capability for cross-sensor cloud detection.

Health monitoring scenarios involve multiple users with conflicting privacy concerns. We envision extended reality facilitating smooth interactions among users while resolving potential conflicts arising from users' conflicting goals, ensuring that sensitive information is not unintentionally revealed. We contribute to an adaptive approach to resolving decision conflicts and content sharing and a scenario-centric access control model with a strategy mediator.

Because of the population aging, homecare monitoring systems and assisted living technologies have been promoted by researchers and industries to help patients and the elderly at home to get medical help in time. Nevertheless, these systems and technologies usually come up with ethics problems and involve patient's privacy concerns. Even if privacy-enhancing technologies have been introduced to the monitoring systems to help protect patient's privacy, it remains a problem for researchers to figure out individual's privacy attitudes and behaviors when being monitored. Since patients have different privacy attitudes and preferences, not only technical staff but also health care providers should take patient's privacy concerns into account when making medical decisions and provide clinical help. In this paper, we are going to discuss a preliminary study about patient privacy decision-making we carried out recently. After analyzing the results, we conducted a follow-up study to figure out the reasons for the results.