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Abstract Multi-dimensional heterojunction materials have attracted much attention due to their intriguing properties, such as high efficiency, wide band gap regulation, low dimensional limitation, versatility and scalability. To further improve the performance of materials, researchers have combined materials with various dimensions using a wide variety of techniques. However, research on growth mechanism of such composite materials is still lacking. In this paper, the growth mechanism of multi-dimensional heterojunction composite material is studied using quasi-two-dimensional (quasi-2D) antimonene and quasi-one-dimensional (quasi-1D) antimony sulfide as examples. These are synthesized by a simple thermal injection method. It is observed that the consequent nanorods are oriented along six-fold symmetric directions on the nanoplate, forming ordered quasi-1D/quasi-2D heterostructures. Comprehensive transmission electron microscopy (TEM) characterizations confirm the chemical information and reveal orientational relationship between Sb2S3 nanorods and the Sb nanoplate as substrate. Further density functional theory calculations indicate that interfacial binding energy is the primary deciding factor for the self-assembly of ordered structures. These details may fill the gaps in the research on multi-dimensional composite materials with ordered structures, and promote their future versatile applications. Graphical Abstract

Abstract The aqueous micro batteries (AMBs) are expected to be one of the most promising micro energy storage devices for its safe operation and cost‐effectiveness. However, the performance of the AMBs is not satisfactory, which is attributed to strong interaction between metal ions and the electrode materials. Here, the first AMBs are developed with NH4+ as charge carrier. More importantly, to solve the low conductivity and the dissolution during the NH4+ intercalation/extraction problem of perylene material represented by perylene‐3,4,9,10‐tetracarboxylic dianhydride (PTCDA), the Ti3C2Tx MXene with high conductivity and polar surface terminals is introduced as a conductive skeleton (PTCDA/Ti3C2Tx MXene). Benefitting from this, the PTCDA/Ti3C2Tx MXene electrodes exhibit ultra‐high cycle life and rate capability (74.31% after 10 000 galvanostatic chargedischarge (GCD) cycles, and 91.67 mAh g−1 at 15.0 A g−1, i.e., capacity retention of 45.2% for a 30‐fold increase in current density). More significantly, the AMBs with NH4+ as charge carrier and PTCDA/Ti3C2Tx MXene anode provide excellent energy density and power density, cycle life, and flexibility. This work will provide strategy for the development of NH4+ storage materials and the design of AMBs.

The development of smart wearable electronic devices puts forward higher requirements for future flexible electronics. The design of highly sensitive and high-performance flexible pressure sensors plays an important role in promoting the development of flexible electronic devices. Recently, MXenes with excellent properties have shown great potential in the field of flexible electronics. However, the easy-stacking inclination of nanomaterials limits the development of their excellent properties and the performance improvement of related pressure sensors. Traditional methods for constructing 3D porous structures have the disadvantages of complexity, long period, and difficulty of scalability. Here, the gas foaming strategy is adopted to rapidly construct 3D porous MXene aerogels. Combining the excellent surface properties of MXenes with the porous structure of aerogel, the prepared MXene aerogels are successfully used in high-performance multifunctional flexible pressure sensors with high sensitivity (306 kPa-1), wide detection range (2.3 Pa to 87.3 kPa), fast response time (35 ms), and ultrastability (>20,000 cycles), as well as self-healing, waterproof, cold-resistant, and heat-resistant capabilities. MXene aerogel pressure sensors show great potential in harsh environment detection, behavior monitoring, equipment recovery, pressure array identification, remote monitoring, and human-computer interaction applications.

Abstract Aims Research on the appropriate scale structure and spatial layout of ecological, urban, and agricultural spaces and the effective and methodical development and protection of urban space can provide a solid foundation for the sustainable development of the complex socio-economic urban environment. Methods Based on the perspective of a coupled social-ecological system, this paper constructed an assessment model to determine the ecological protection importance and development suitability of urban space, proposed the ecological-urban-agricultural priority for densely populated urban areas, and established the appropriate functional layout of urban space using a priority matrix-based mapping method. Results Through an empirical case study of Shenzhen, this paper determined the optimal scale structure of the types of functional spaces, identified areas of functional conflict, and proposed targeted optimization strategies for functional mismatch areas. The findings show that the appropriate scale ratio of Shenzhen’s urban, ecological, and agricultural functions is 5:4:1, which is consistent with the actual structure ratio of Shenzhen’s land use trends. Conclusions The proposed method can predict the appropriate scale structure of urban space development and ecological protection based on the comprehensive multi-perspective consideration of social and economic factors to provide a scientific reference for effective decision-making on urban space optimization and sustainable development.

Abstract The construction of the national water network optimizes water resource allocation but also causes a redistribution of pollutants due to water quality differences, posing urgent challenges for water pollution management. Taking the South-to-North Water Diversion Project and Yangtze-to-Hanjiang River Water Diversion Project (YHRWDP) as an example and considering the pressure brought by the redistribution of pollutants to the polluting enterprises (PE) and water diversion enterprises (WDE) situated in areas where pollution is aggravated, a differential game model of pollution control among the central government, water-supply area (WSA), PE and WDE is constructed based on the cost-sharing mechanism. The results show that: (1) The pressure coefficient has an apparent negative correlation with the effort level of the affected subject. (2) An increase in the amount of pollution elimination per unit of pollution control effort of WSA and PE will lead to their own investment in more efforts to improve the ecology of the basin, thus benefiting all participants in the system. (3) The cost-sharing rate of the central government on the WSA of the YHRWDP, the PE, and the WDE is influenced by several factors, which shows a stable monotonically increasing or decreasing relation. The results are an useful supplement to the research on natural river pollution control, and can also provide a reference for designing pollution control mechanisms of national water network.

Abstract Background Plasma ceramide plays a potentially significant role in the pathogenesis of coronary microvascular dysfunction. However, the relationship between plasma ceramide and coronary microvascular resistance in patients remains unclear. This study aimed to evaluate the association between plasma ceramide levels, as well as their distinct ratios, and coronary microvascular resistance. Methods This single-center observational study retrospectively enrolled patients who underwent both ceramide measurement and coronary angiography during hospitalization. The microvascular resistance of the coronary arteries was assessed in all patients using the angiography-derived index of microcirculatory resistance (Angio-IMR). The cumulative coronary microvascular resistance was calculated by summing the microvascular resistance of the three main coronary arteries. Multiple linear and logistic regression analyses were employed to evaluate the relationship between plasma ceramide and cumulative coronary microvascular resistance. Restricted cubic spline (RCS) analysis was conducted to investigate the association between plasma ceramide levels and cumulative coronary microvascular resistance. Receiver operating characteristic (ROC) curves were employed to evaluate the predictive value of plasma ceramide for coronary microvascular resistance. Additionally, subgroup analyses and interaction tests were performed. Results A total of 225 patients were included in this study, with a median cumulative coronary microvascular resistance of 48.04 (40.32–56.73). After adjusting for potential confounding factors, both plasma 16:0 ceramide and the 16:0/24:0 ceramide ratio were positively associated with cumulative coronary microvascular resistance [standardized β ± standard error: 75.05 ± 8.46 (P

Abstract Purpose Immune checkpoint inhibitors (ICIs) have improved lung cancer treatment but are associated with immune-related adverse events (irAEs). This study analyzes the relationship between irAEs and treatment effectiveness in advanced non-small cell lung cancer (NSCLC) patients. Methods We conducted a retrospective study of NSCLC patients treated with ICIs from March 2019 to October 2022 at Zhongshan Hospital (Xiamen). Patients were divided into irAE and non-irAE groups, and treatment outcomes were compared. Results A total of 154 patients were included, with 36.4% in the irAE group and 63.6% in the non-irAE group. Most irAEs were Grade 1–2 (86.4%), with 13.6% being Grade 3 or higher. The irAE group had higher disease control rates (DCR: 94.6% vs. 76.5%, P = 0.004) and objective response rates (ORR: 42.9% vs. 26.5%, P = 0.037). Median progression-free survival (PFS) was longer in the irAE group (18 vs. 9 months, HR: 0.53, P = 0.001), as was overall survival (OS: 39.5 vs. 16 months, HR: 0.46, P = 0.001). Landmark analysis at 6 and 12 weeks confirmed that irAEs were associated with improved outcomes. Moreover, patients who experienced two or more adverse events during treatment had significantly longer OS compared to those who had only one or no adverse events (41.6 months vs. 34.0 vs. 23.6, P = 0.003). Conclusion Patients with irAEs demonstrated better outcomes, including ORR, DCR, PFS, and OS. Further studies on biomarkers and irAE incidence are warranted to improve lung cancer management.

ABSTRACT Purpose Anlotinib is widely used in the treatment of lung cancer. However, there remains a lack of predictive biomarkers to effectively gauge the response to anlotinib therapy. We conducted a retrospective study to preliminarily explore potential risk factors that might predict outcomes in lung cancer patients undergoing anlotinib treatment. Patients and Methods We retrospectively analyzed lung cancer patients treated with anlotinib at our hospital between 1 June 2018 and 1 June 2021. Data were gathered from electronic medical records. Demographic and clinical characteristics of patients, progression‐free survival (PFS), and overall survival (OS) were described. Predictive factors related to treatment efficacy were preliminarily analyzed using Cox regression and Kaplan–Meier survival analyses. Results After adjusting for potential confounders, clinical stage IV (hazard ratio [HR] = 2.52, 95% confidence interval [CI], 1.09–5.82, p = 0.0311), N‐terminal fragment brain natriuretic peptides (NT‐pro‐BNP) > 300 pg/mL (HR = 2.54, 95% CI, 1.17–5.52, p = 0.0183), and neuron‐specific enolase (NSE) > 16.3 ng/mL (HR = 1.70, 95% CI, 1.03–2.81, p = 0.0389) were associated with shorter OS, whereas age (HR = 0.96, 95% CI, 0.94–0.99, p = 0.0055) was associated with a longer PFS in fully adjusted model. Kaplan–Meier analyses of cumulative risk factors (clinical stage IV, NT‐pro‐BNP > 300 pg/mL, and NSE > 16.3 ng/mL) indicated that patients with a greater number of coexisting risk factors had significantly shorter OS (p

BackgroundAsthma is associated with an increased risk of cardiovascular mortality, potentially influenced by dietary phosphorus intake through its effects on inflammation and oxidative stress.MethodsData from 7,539 asthma patients in the National Health and Nutrition Examination Survey (NHANES) 1999–2018 cohort were analyzed using weighted Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CI). Kaplan-Meier survival curves and a nomogram were used to assess survival probabilities and individualized risk, while restricted cubic spline (RCS) analysis evaluated non-linear dose-response relationships. Sensitivity analyses were conducted to test the robustness of the findings.ResultsHigher dietary phosphorus intake was associated with reduced cardiovascular mortality (HR: 0.43; 95% CI: 0.22–0.85 for the highest vs. lowest quartile; p for trend = 0.018). Kaplan-Meier curves showed improved survival with increasing phosphorus intake, a result consistently supported by subgroup analyses. RCS analysis confirmed a non-linear dose-response relationship, identifying a threshold at 1,861.52 mg/day, below which higher phosphorus intake was significantly associated with lower cardiovascular mortality. However, above this threshold, the protective effect diminished. Sensitivity analyses further validated these results.ConclusionElevated dietary phosphorus intake is associated with reduced cardiovascular mortality in asthma patients, suggesting its potential as a dietary intervention.

The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods.

Strong earthquakes can frequently trigger a large number of co-seismic landslides. Obtaining an accurate susceptibility map of co-seismic landslides is crucial for post-disaster rescue and reconstruction efforts. In this study, the pre-seismic average annual surface deformation rate of the study area was obtained using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. Furthermore, the landslide hazards prior to the earthquake in multiple locations within the study area were analyzed. Subsequently, the deformation data was combined with 11 evaluation factors, including the distance to the fault and the Peak Ground Acceleration (PGA), to model the susceptibility of landslides. We constructed four models to evaluate the susceptibility of landslides in the study area: the Bayesian optimization random forest (BO-RF) model, the random forest model (RF), the logistic regression model (LR), and the support vector machine model (SVM). The BO-RF model outperformed the other models, achieving an AUC value of 0.984, an accuracy of 0.952, a precision of 0.953, a recall of 0.952, and an F1 score of 0.953. Furthermore, incorporating pre-seismic deformation features in the evaluation of co-seismic landslide susceptibility can effectively improve the reliability of model predictions, as compared to the evaluation model results without incorporating deformation factors. The obtained research results provide valuable data support for the rescue and management of disaster-stricken areas.

Objectives To develop and validate a clinical diagnostic model based on optical pumped magnetometer magnetocardiography (OPM-MCG) for the detection of myocardial ischaemia in patients with borderline coronary lesions prior to invasive coronary angiography (ICA).Design Prospective observational cohort study.Setting Single centre of the China National Clinical Research Centre for Cardiovascular Disease (NCCMRC).Participants Adults with borderline coronary lesions on ICA (n=141).Interventions Underwent OPM-MCG before ICA and fractional flow reserve measurement.Results Five parameters were included in the final diagnostic model: MAgmax-TT, δDtsum-PN, δAgsum-C, δArsum-N and δArmin-N. 1000 bootstrap replications showed that the area under the receiver operating characteristic curve and 95% CI of the diagnostic model were 0.864 (0.803–0.925), with a sensitivity of 79.4%, specificity of 80.8%, positive predictive value of 79.4% and negative predictive value of 80.8%. Decision curve analysis showed a net benefit from the predictive model when the threshold probability of an ischaemic patient was >12%, suggesting the potential utility of the model in the real world.Conclusions A nomogram based on five OPM-MCG parameters was developed to assess myocardial ischaemia in patients with borderline coronary lesions and has the potential to reduce the need for unnecessary ICA.Trial registration number China Clinical Trial Registry (ChiCTR2300072382).

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of “population heat—land use scale” to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity.

Abstract Background Tuberculosis (TB), a highly contagious respiratory disease, presents a significant global health threat, with a notable increase in incidence reported by the WHO in 2022. Particularly, the interplay between TB and non-small cell lung cancer (NSCLC) gains attention, especially considering the rising use of immune checkpoint inhibitors (ICIs) in cancer treatment. This interplay may influence TB diagnostics and reactivation, warranting a closer examination. Methods A retrospective analysis was conducted on clinical data of NSCLC patients with positive T-SPOT results before undergoing anti-tumor treatment at Zhongshan Hospital (Xiamen), Fudan University, from January 1, 2021 to December 31, 2022. We assessed the incidence of tuberculosis reactivation and treatment outcomes among these patients. Moreover, we compared the differences in tuberculosis activity between the ICIs and non-ICIs treatment groups. Additionally, we observed the changes in T-SPOT spot count before and after immunotherapy, analyzing their association with tuberculosis activity and prognosis. Results A total of 40 NSCLC patients with positive T-SPOT results before treatment were included in the study, with 26 patients in the ICIs treatment group and 14 patients in the non-ICIs treatment group. The study found no significant differences between the two groups in terms of gender, age, stage, histological type, performance status, driver gene expression, and distant metastasis. With a median follow-up time of 10.0 (6.0-14.5) months, three cases (11.5%) in the ICIs treatment group developed tuberculosis activity, diagnosed at 2, 3, and 12 months after ICIs treatment initiation. Conversely, no tuberculosis activity was observed in the non-ICIs treatment group, and the difference between the two groups was not significant (P = 0.186). Among the 32 patients who received ICIs treatment, spot count dynamics were diverse: four cases (12.5%) showed an increase, 12 cases (37.5%) had no change, and 16 cases (50.0%) had a decrease. During the follow-up, the progression rate (PD) was 50.0%, 75.0%, and 62.5% in the three groups, respectively (P = 0.527). Similarly, the mortality rate was 0%, 25.0%, and 25.0%, respectively (P = 0.106). Interestingly, among the patients with decreased spot counts, three cases (18.75%) were diagnosed with active pulmonary tuberculosis. Conclusions For NSCLC patients with a positive T-SPOT response undergoing ICIs treatment, our study observed indications of active tuberculosis. The varied T-SPOT spot count changes post-ICIs treatment suggest a complex interaction, potentially linking T-SPOT spot count reduction to tuberculosis reactivation risk. These preliminary findings underscore the importance of further research to more accurately assess T-SPOT’s diagnostic utility in this context.

The accurate prediction of tunneling speed in tunnel boring machine (TBM) construction is the basis for the timely adjustment of the operating parameters of TBM equipment to ensure safe and efficient tunneling. In this paper, a multi-step prediction model of TBM tunneling speed based on the EWT-ICEEMDAN-SSA-LSTM hybrid model is proposed. Firstly, four datasets were selected under different geological conditions, and the original data were preprocessed using the binary discriminant function and the 3σ principle; secondly, the preprocessed data were decomposed using the empirical wavelet variation (EWT) to obtain several subseries and residual series; then, Intrinsic Computing Expressive Empirical Mode Decomposition With Adaptive Noise (ICEEMDAN) was used to perform further decomposition on residual sequences. Finally, several subsequences were fed into a Long Short-Term Memory (LSTM) network optimized by the Sparrow Search Algorithm (SSA) for multi-step training and prediction, and the predicted results of each subsequence were added up to obtain the final result. A comparison with existing models showed that the performance of the prediction method proposed in this paper is superior to other models. Of the four datasets, the average accuracy from the first step prediction to the fifth step prediction reached 99.06%, 98.99%, 99.07%, and 99.03%, respectively, indicating that the proposed method has high multi-step prediction performance and generalization ability. In this sense, this paper provides a reference for other projects.

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.

ObjectiveThis study investigates the association between convalescent plasma therapy and the negative conversion rate in patients with persistent COVID-19 test positivity.MethodA retrospective analysis was conducted on patients with severe or mild to moderate COVID-19 whose viral nucleic acid tests remained positive for over 30 days. Patients were categorized into two groups: those who administered convalescent plasma therapy and those who were not. Data collected included information on therapy strategies used (convalescent plasma, corticosteroids, interferons, etc.), patients’ demographic characteristics, comorbidities, therapeutic medications, and nucleic acid testing results. Patients in the convalescent plasma therapy group were matched 1:2 ratio with those in the non-convalescent plasma therapy group. Cumulative negative conversion rates on the fifth, tenth, and fifteenth days post-therapy initiation were analyzed as dependent variables. Independent variables included therapy strategies, demographic characteristics, comorbidities, and therapeutic medication usage. Univariate analysis was conducted, and factors with a p-value (P) less than 0.2 were included in a paired Cox proportional hazards model.ResultsThere was no statistically significant difference in the cumulative negative conversion rate between the convalescent plasma therapy group and the non-convalescent plasma therapy group on the fifth, tenth, and fifteenth days. Specifically, on day the fifth, the negative conversion rate was 41.46% in the convalescent plasma therapy group compared to 34.15% in the non-convalescent plasma therapy group (HR: 1.72, 95% CI: 0.82–3.61, P = 0.15). On the tenth day, it was 63.41% in the convalescent plasma therapy group and 63.41% in the non-convalescent plasma therapy group (HR: 1.25, 95% CI: 0.69∼2.26, P = 0.46). On the fifteenth day, the negative conversion rate was 85.37% in the convalescent plasma therapy group and 75.61% in the non-convalescent plasma therapy group (HR: 1.19, 95% CI: 0.71–1.97, P = 0.51).ConclusionOur finding does not support the hypothesis that convalescent plasma therapy could accelerate the time to negative conversion in patients who consistently test positive for COVID-19.

Introduction: Highly clustered color steel buildings (CSB) have become new impervious surfaces and heat island areas in cities due to their materials, especially in the Northwest. However, the extent of the influence of CSB on land surface temperature (LST) and how they are quantitatively related to each other have not been explored.Methods: Therefore, this paper takes Urumqi city, which is densely populated with CSB and has a unique topography, as an example. We obtained LST data and CSB data based on Landsat and Google Earth images, and quantitatively analyzed the warming effect of the CSB on the urban LST and the characteristics of regional variability.Results and discussion: The results of the study showed that from 2005 to 2020, the scale of CSB in Urumqi City has been expanding, with high-density aggregation shifting from urban areas to the suburbs. The urban area of Urumqi city shows a “cool island effect”, with higher temperatures in CSB. The high-density distribution of CSB roughly coincides with the high temperature areas of the city. The density of CSB is highly positively correlated with LST, with the correlation coefficient reaching 0.973 in 2010. For every 0.2 increase in cluster density, the corresponding temperature can rise by 0.4°C. At the same density of aggregation, the average temperature of urban CSB is lower than that in the suburbs. The contribution index of suburban CSB to LST reaches 0.512, significantly higher than the surrounding other buildings, marked it one of the main contributors to the thermal environment in the suburbs.The research results can provide a reference and data support for the sustainable development and planning of cities.

Heterogeneous high-resolution remote sensing image matching will be disturbed by the differences in sensor type, imaging angle, height, and imaging time, and the matching difficulty is further increased in complex scenes with dense urban buildings and noticeable height differences. This article proposes a method for matching heterogeneous high-resolution remote sensing images based on partitioned feature extraction and three-dimensional spatial constraints. First, this article conducts image partitioning based on the geometric differences of ground objects. Two feature extraction methods, namely adaptive phase threshold and weighted moment map, are employed to extract feature points independently. To address the issue of inaccurate feature descriptions caused by drastic changes in viewing angles in buildings, we construct a robust feature descriptor by combining a multiscale phase weighted energy convolution histogram with a new gradient location orientation histogram-like local feature descriptor. In addition, a new similarity measure incorporating three-dimensional spatial constraints and the marginalizing sample consensus method is applied to eliminate mismatched point pairs, ensuring the acquisition of precise matching points. Based on the feature detection results of two different synthetic data sets, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. Ultimately, the matching performance is experimentally verified on six groups of heterogeneous high-resolution remote sensing images. The experimental results show that the proposed method significantly outperforms RIFT, HAPCG, and MS-HLMO methods and achieves the best matching accuracy results.