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Optical remote sensing images are of considerable significance in a plethora of applications, including feature recognition and scene semantic segmentation. However, the quality of remote sensing images is compromised by the influence of various types of noise, which has a detrimental impact on their practical applications in the aforementioned fields. Furthermore, the intricate texture characteristics inherent to remote sensing images present a significant hurdle in the removal of noise and the restoration of image texture details. In order to address these challenges, we propose a feature interaction complementary learning (FICL) strategy for remote sensing image denoising. In practical terms, the network is comprised of four main components: noise predictor (NP), reconstructed image predictor (RIP), feature interaction module (FIM), and fusion module. The combination of these modules serves to not only complete the fusion of the prediction results of NP and RIP, but also to achieve a deep coupling of the characteristics of the two predictors. Consequently, the advantages of noise prediction and reconstructed image prediction can be combined, thereby enhancing the denoising capability of the model. Furthermore, comprehensive experimentation on both synthetic Gaussian noise datasets and real-world denoising datasets has demonstrated that FICL has achieved favorable outcomes, emphasizing the efficacy and robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

The range–Doppler (R–D) model is extensively employed for the geometric processing of synthetic aperture radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R–D model, comprising a process that involves numerous parameters and complex computations. In order to reduce the specialization and complexity of parameter optimization in the classic R–D model, we introduced a novel approach called ICRD (image compensation-based range–Doppler) to improve the positioning accuracy of the R–D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also designed low-order polynomial compensation models with different parameters. The models were evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and Gaofen3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of ground control points (GCPs) were conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved, even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R–D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric-positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images. [ABSTRACT FROM AUTHOR]

This article proposes a method for ontology construction in the field of ancient architecture digitization with the aim of addressing the lack of formalization, sharing, and reusable unified description mechanisms currently observed in the field of ancient architecture digitization. This method defines the related concepts, attributes, and relationships between concepts in the digitization of ancient architecture. It employs the network ontology language OWL to model the ontology in the digitization domain of ancient architecture and realizes the visualization of the ontology in the digitization domain of ancient architecture, thereby providing effective support for the sharing and reuse of digitization knowledge of ancient architecture. Finally, an example of a wooden tower is taken to verify the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Natural disaster risk monitoring is an important task for disaster prevention and reduction. In the case of immovable cultural relics, however, the feedback mechanism, risk factors, monitoring logic, and monitoring indicators of natural disaster risk monitoring are complex. How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers. Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics, this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin. This framework focuses on risk monitoring, including the physical entities of natural disaster risk for immovable cultural relics, monitoring indicators, and virtual entity construction. A platform for monitoring the natural disaster risk of immovable cultural relics is proposed. Using the Puzhou Ancient City Site as a test bed, the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) pose a significant threat to human health, and it is essential to develop advanced VOC gas sensors by understanding the mechanisms. Metal oxide semiconductor gas sensors have the advantages of high sensitivity, high-temperature resistance, stability, and safety. The rational design of crystal facets and doping components can improve the sensing properties. However, the time-consuming experimental optimization of the sensor design has tremendously inhibited the development of the new sensing material. Nevertheless, density functional theory (DFT) calculations shined a light on the fundamental understanding of the sensing mechanism at the molecular level and accelerated the sensor design process. In this study, we used DFT calculations to investigate the sensing properties of the P-doped Co3O4(111) surface toward methanol, methane, formic acid, water, and formaldehyde. Results showed that P doping changed the surface electronic distribution, increased the charge transfer of HCHO, and enhanced the adsorption energy of other molecules. The significant difference in adsorption energy between H2O and HCHO indicates that the P-doped surface exhibits certain antihumidity properties in HCHO sensing. Our work broadens the application of P-doped Co3O4(111) in the VOC gas-sensing field and provides a theoretical basis for designing metal oxide semiconductor gas sensors. [ABSTRACT FROM AUTHOR]

Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can't separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can't be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. [ABSTRACT FROM AUTHOR]