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Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera's attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds. [ABSTRACT FROM AUTHOR]

The Curie Point Depth (CPD) marks a significant temperature boundary (∼580°C) within the Earth's lithosphere. However, there has been ongoing debate regarding its spatial distribution. In this research, we utilized the Equivalent Source Method (ESM) based on Gauss‐Legendre integration and data obtained from the EMM2017 model, along with a five‐layer susceptibility model, to generate a 0.5° × 0.5° grid of continental CPD distribution for China and surroundings. The average CPD in the study area is 30.4 km, which is slightly shallower than the average depth of global continental Moho (∼33 km). Notably, stable and cold cratonic basins, such as the Tarim Basin and the Sichuan Basin, exhibit deep CPD of ∼45 km. In contrast, the North China Craton, which has experienced significant tectono‐thermal activity since the Late Mesozoic, shows moderate CPD of ∼30 km and a gradual uplift from west to east. The Tuva‐Mongol orocline within the Central Asian Orogenic Belt, the Deccan Volcanic Province in the Indian subcontinent and the Eastern Yangtze Craton have shallow CPD of ∼20 km. We estimate the surface heat flow by CPD, and the result is consistent with measurements within a RMSE of 18.1 mW/m2. When comparing the CPD with Moho, we find that the CPD may lie below Moho in stable and cold cratonic areas. In comparison to two recent global CPD models, our regional model demonstrates better alignment with tectonic features. Plain Language Summary: The Curie Point Depth (CPD) is the depth where crustal temperatures equal the Curie temperature of magnetite (∼580°C), the dominant magnetic mineral in the crust, and hence below which there is no magnetization. It is a crucial temperature boundary within the Earth's lithosphere, providing valuable insights into the lithospheric thermal structure and dynamic evolution. Traditionally, the CPD is mostly inverted by the Power Spectral Density (PSD) method in the frequency domain, which is more suitable for shallow CPD regions. Here, we determine a new 0.5° × 0.5° continental CPD model for China and surroundings utilizing an Equivalent Source Method (ESM) in the spatial domain. Then we estimate the surface heat flow and crustal temperature with this CPD model, and discuss its relation with the Moho interface. Compared to two recent global models, our regional CPD model showcases enhanced consistency with tectonic characteristics. Key Points: We inverted a new regional continental Curie Point Depth model via Equivalent Source MethodBased on the new CPD model and measured thermal parameters, the surface heat flow and crustal temperature distribution are derivedOur new regional CPD model exhibits better consistency with the tectonics compared to two recent global models [ABSTRACT FROM AUTHOR]

In this paper, we focus on a class of robust vector polynomial optimization problems (RVPOP in short) without any convex assumptions. By combining/improving the utopia point method (a nonlinear scalarization) for vector optimization and "joint+marginal" relaxation method for polynomial optimization, we solve the RVPOP successfully. Both theoratical and computational aspects are considered. [ABSTRACT FROM AUTHOR]

Years of geomagnetic observatory data during the geomagnetic quiet days (Kp ≤ 3) at the low and middle dip latitudes from INTERMAGNET and SuperMAG were utilized. Seasonal models of Sq currents for two longitudinal chains at each year and under two solar activity levels (F10.7 index are ∼75 and 125 sfu) were constructed by spherical harmonic analysis. It is found that there are significant seasonal variations of Sq currents in the Asian‐Oceanian (A‐O) and North‐South American (N‐SA) longitude sectors under different solar activities. First, the focus intensity J increases faster in local winter than in summer for both two hemispheres as solar activity strengthens. J of ionospheric currents in the northern hemisphere (NH) during local winter is higher than that in the southern hemisphere during its winter. This asymmetric activity in the N‐SA chain increases with the solar activity becomes stronger. Second, with solar activity increases, the ionospheric current focus of N‐SA chain in the NH shifts toward the lower latitudes during two solstices, while internal current focus of both two chains move toward the lower latitudes during the December solstice. Seasonal variations of Sq currents in the two chains exhibit longitudinal effects. With increasing solar activity, the two hemispheric focuses of the ionospheric currents in the A‐O chain both move closer to the midday longitude sector, while in the N‐SA chain, only the focus in the NH shifts toward noon. The results reveal details of the seasonal variations of Sq currents. Key Points: Seasonal models of Sq currents at low and middle latitudes were constructed by spherical harmonic analysis in two longitudinal chainsThe characteristics of the seasonal variations and their dependence on solar activity for the A‐O and North‐South American chains were obtainedThe longitudinal effects of the seasonal variations in Sq currents were revealed [ABSTRACT FROM AUTHOR]

In this paper, we are interested in the study of approximate optimality conditions for weakly ϵ-efficient solutions to robust multi-objective optimization problems ((RMOP) for short) in view of its associated minimax optimization problem (MMOP). To this end, we first establish the relationship between a weakly ϵ-efficient solution to the problem (RMOP) and an α-solution to the problem (MMOP), where ϵ = (ϵ 1 , ... , ϵ p) ∈ R + p ∖ { 0 } and α = max j = 1 , ... , p { ϵ j }. Then, we explore the representation of the so-called β-normal set (where β ≥ 0 is a given parameter) to a closed convex set at some reference point by two methods. At last, by employing the α-subdifferential of the max-function and the obtained representation of the β-normal set, we establish an approximate necessary optimality condition for the problem (RMOP). Moreover, we also give an example to illustrate our results. [ABSTRACT FROM AUTHOR]