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Retrieving UAV images that lack POS information with georeferenced satellite orthoimagery is challenging due to the differences in angles of views. Most existing methods rely on deep neural networks with a large number of parameters, leading to substantial time and financial investments in network training. Consequently, these methods may not be well-suited for downstream tasks that have high timeliness requirements. In this work, we propose a cross-view remote sensing image retrieval method based on transformer and visual foundation model. We investigated the potential of visual foundation model for extracting common features from cross-view images. Training is only conducted on a small, self-designed retrieval head, alleviating the burden of network training. Specifically, we designed a CVV module to optimize the features extracted from the visual foundation model, making these features more adept for cross-view image retrieval tasks. And we designed an MLP head to achieve similarity discrimination. The method is verified on a publicly available dataset containing multiple scenes. Our method shows excellent results in terms of both efficiency and accuracy on 15 sub-datasets (10 or 50 scene categories) derived from the public dataset, which holds practical value in engineering applications with streamlined scene categories and constrained computational resources. Furthermore, we initiated a comprehensive discussion and conducted ablation experiments on the network design to validate its efficacy. Additionally, we analyzed the presence of overfitting within the network and deliberated on the limitations of our study, proposing potential avenues for future enhancements.

For UAV large oblique image geo-localization in the dense buildings area, there are still two main challenges. One is the presence of obvious occlusion and large viewpoint differences in UAV images, and the other arises from the fact that reference images, particularly orthographic satellite images, lack façade information of man-made structures (such as buildings and roads), which is crucial for UAV large oblique images. Most of existing image-based geo-localization methods only address the first challenge, neglecting the interference brought by the second challenge, especially for UAV large oblique image geo-localization in the dense buildings area. Motivated by both these two challenges, we have proposed a novel method for UAV large oblique image geo-localization in the dense buildings areas, with the segments direction statistics (SDS) features and their histogram descriptors designed. By considering both the local and global features of man-made structures, the proposed method effectively addresses the significant information difference encountered in cross-view image matching. We conducted experiments on both the public UAV images dataset University-1652 and our own collected dataset of UAV large oblique long focal whiskbroom (LO-LF-W) images. Comparative analysis with state-of-the-art (SOTA) methods demonstrated that the proposed method improves the geo-localization accuracy by approximately 10%. Furthermore, the proposed method exhibits greater robustness to noise and changing orientation of reference images, making it particularly well-suited for dense buildings areas that pose challenges for existing methods.

Organosulfates (OSs) are ubiquitous aerosol components, which has seen intense research over years. However, spatial and diurnal variations in OS formation in polluted atmospheres remain poorly understood. In this study, 130 OS species were quantified (or semi-quantified) in ambient fine particulate matter (PM2.5) collected in urban and suburban Shanghai (East China) in the summer of 2021. Isoprene- and monoterpene-derived OSs were dominant OS groups (averaging 51 % and 19 % of total quantified OSs, respectively), likely indicating a large biogenic contribution to OS formation in summer. Most OSs peaked during daytime, while monoterpene-derived nitrooxy-OSs (NOSm) increased during nighttime. Accordingly, OSs were largely produced via daytime formation processes, rather than nighttime chemistry, except for NOSm. Additionally, although OS formation in the urban and suburban areas exhibited similar diurnal variations, the average concentrations of biogenic and anthropogenic OSs decreased significantly from the urban site to the suburban site. Furthermore, we concretized daytime OS formation based on the interactions among OSs, ultraviolet (UV), ozone (O3), and sulfate (SO42-). Indeed, the concentrations of most OSs were significantly correlated with the values of UV[O3][SO42-] during daytime in both urban and suburban Shanghai. In particular, the correlation between major OSs and UV[O3][SO42-] was stronger than the correlation of major OSs with O3 and SO42-; moreover, there was no significant correlation between major OSs and UV. Thus, higher urban OS events were attributed to the enhanced photochemical processes and sulfate level in the urban area. Overall, this study provides field evidence for the influence of photochemical processes and anthropogenic sulfate on OS formation and has important implications for the mitigation of organic particulate pollution.

The atmospheric oxidation of dimethyl sulfide (DMS) represents a major natural source of atmospheric sulfate aerosols. However, there remain large uncertainties in our understanding of the underlying chemistry that governs the product distribution and sulfate yield from DMS oxidation. Here, chamber experiments were conducted to simulate gas-phase OH-initiated oxidation of DMS under a range of reaction conditions. Most importantly, the bimolecular lifetime (τbi) of the peroxy radical CH3SCH2OO was varied over several orders of magnitude, enabling the examination of the role of peroxy radical isomerization reactions on product formation. An array of analytical instruments was used to measure nearly all sulfur-containing species in the reaction mixture, and results were compared with a near-explicit chemical mechanism. When relative humidity was low, “sulfur closure” was achieved under both high-NO (τbi<0.1 s) and low-NO (τbi>10 s) conditions, though product distributions were substantially different in the two cases. Under high-NO conditions, approximately half the product sulfur was in the particle phase, as methane sulfonic acid (MSA) and sulfate, with most of the remainder as SO2 (which in the atmosphere would eventually oxidize to sulfate or be lost to deposition). Under low-NO conditions, hydroperoxymethyl thioformate (HPMTF, HOOCH2SCHO), formed from CH3SCH2OO isomerization, dominates the sulfur budget over the course of the experiment, suppressing or delaying the formation of SO2 and particulate matter. The isomerization rate constant of CH3SCH2OO at 295 K is found to be 0.13±0.03 s−1, in broad agreement with other recent laboratory measurements. The rate constants for the OH oxidation of key first-generation oxidation products (HPMTF and methyl thioformate, MTF) were also determined (kOH+HPMTF=2.1×10-11 cm3 molec.−1 s−1, kOH+MTF=1.35×10-11 cm3 molec.−1 s−1). Product measurements agree reasonably well with mechanistic predictions in terms of total sulfur distribution and concentrations of most individual species, though the mechanism overpredicts sulfate and underpredicts MSA under high-NO conditions. Lastly, results from high-relative-humidity conditions suggest efficient heterogenous loss of at least some gas-phase products.

Compound flooding is generated when two or more flood drivers occur simultaneously or in close succession. Multiple drivers can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers would facilitate a more accurate assessment of compound flood risk in coastal regions. This study employed the D-Flow Flexible Mesh model to simulate the historical peak coastal water level, consisting of the storm surge, astronomical tide, and relative sea level rise (RSLR), in Shanghai over the period 1961–2018. It then applies a copula-based methodology to calculate the joint probability of peak water level and rainfall during historical tropical cyclones (TCs) and to calculate the marginal contribution of each driver. The results indicate that the astronomical tide is the leading driver of peak water level, followed by the contribution of the storm surge. In the longer term, the RSLR has significantly amplified the peak water level. This study investigates the dependency of compound flood events in Shanghai on multiple drivers, which helps us to better understand compound floods and provides scientific references for flood risk management and for further studies. The framework developed in this study could be applied to other coastal cities that face the same constraint of unavailable water level records.

Initial global localization of a mobile robotic platform is the foundation for its navigation and mapping, especially when the platform enters into unknown environments. In GNSS-denied indoor scenes, LiDAR is widely used for robot localization, especially in indoor scenes with poor lighting. In most existing LiDAR-based initial global localization methods, it is necessary to build the point cloud reference map in advance, which costs a large quantity of manpower and time. For this reason, a LiDAR-based initial global localization method using imperfect architectural skeleton information is proposed in this work. Firstly, we propose a lightweight management scheme for collected imperfect architectural information, which is convenient for efficient registration with real scans. Secondly, we extract architectural skeletons (stable man-made structures such as walls and columns) from both architectural information and real scans, and design them as line pairs feature patterns like P-LP, V-LP and C-LP. Thirdly, we propose a matrix descriptor for line pairs feature patterns description and initial matching. Finally, we construct error equations to estimate the pose by initial matching line pairs, and acquire the optimal localization results with the highest hit ratio on architectural grid map. A mobile robotic platform with the 16 beam LiDAR is experimented in typical indoor scenes such as rooms, corridors and undergrounding parking lots. Experiments show that the success rate of initial global localization reaches 80%, the average position error is about 0.10m and the running time is about 400ms per 1000 scans, which meet the requirements of indoor autonomous driving.

Using images from UAV oblique ultra-long focal small view field whiskbroom camera (ULF-SVF-WC) system for object positioning and mapping is more difficult than conventional aerial photogrammetry, for the particularity of oblique ULF-SVF-WC imaging mode. Therefore, the precision and accuracy of its object positioning are also quite different from that of the conventional UAV photography. In this paper, we analysed the accuracy of real-time object positioning without ground control points (GCPs) for images from UAV oblique ULF-SVF-WC System. Firstly, we studied the imaging principles and characteristics of the oblique ULF-SVF-WC system. Then, we established the coordinate transformation relationship from the object point to the image point and constructed a strict imaging model for oblique ULF-SVF-WC image, which was used for real-time single-ray back-projection positioning assisted by DEM. Thirdly, we quantitatively analysed the distribution and variation of the oblique ULF-SVF-WC image single-ray back-projection errors in theory based on error propagation law and simulation data. Finally, we conducted the experiment of real oblique ULF-SVF-WC flight images for the actual positioning accuracy analysis. The experiment results showed that: the influence of system error on positioning generally conforms to the distribution and variation of theoretical precision, that is, the accuracy of scanning direction is much lower than that of the flight direction; while the accuracy of actual single-ray back-projection positioning is evidently lower than that of the theoretical analysis and there are obvious system errors in the positioning residuals. It indicates that the 6 external orientation elements calculated from POS data contains obvious system error whose influence is greater than random error and this should be eliminated in real-time single-ray back-projection for object positioning.

Many efforts have been devoted to quantifying the impact of intercontinental transport on global air quality by using global chemical transport models with horizontal resolutions of hundreds of kilometers in recent decades. In this study, a global online air quality source–receptor model (GNAQPMS-SM) is designed to effectively compute the contributions of various regions to ambient pollutant concentrations. The newly developed model is able to quantify source–receptor (S-R) relationships in one simulation without introducing errors by nonlinear chemistry. We calculate the surface and planetary boundary layer (PBL) S-R relationships in 19 regions over the whole globe for ozone (O3), black carbon (BC), and non-sea-salt sulfate (nss-sulfate) by conducting a high-resolution (0.5&deg &times 0.5&deg) simulation for the year 2018. The model exhibits a realistic capacity in reproducing the spatial distributions and seasonal variations of tropospheric O3, carbon monoxide, and aerosols at global and regional scales – Europe (EUR), North America (NAM), and East Asia (EA). The correlation coefficient (R) and normalized mean bias (NMB) for seasonal O3 at global background and urban–rural sites ranged from 0.49 to 0.87 and −2 % to 14.97 %, respectively. For aerosols, the R and NMB in EUR, NAM, and EA mostly exceed 0.6 and are within &pm;15 %. These statistical parameters based on this global simulation can match those of regional models in key regions. The simulated tropospheric nitrogen dioxide and aerosol optical depths are generally in agreement with satellite observations. The model overestimates ozone concentrations in the upper troposphere and stratosphere in the tropics, midlatitude, and polar regions of the Southern Hemisphere due to the use of a simplified stratospheric ozone scheme and/or biases in estimated stratosphere–troposphere exchange dynamics. We find that surface O3 can travel a long distance and contributes a non-negligible fraction to downwind regions. Non-local source transport explains approximately 35 %–60 % of surface O3 in EA, South Asia (SAS), EUR, and NAM. The O3 exported from EUR can also be transported across the Arctic Ocean to the North Pacific and contributes nearly 5 %–7.5 % to the North Pacific. BC is directly linked to local emissions, and each BC source region mainly contributes to itself and surrounding regions. For nss-sulfate, contributions of long-range transport account for 15 %–30 % within the PBL in EA, SAS, EUR, and NAM. Our estimated international transport of BC and nss-sulfate is lower than that from the Hemispheric Transport of Air Pollution (HTAP) assessment report in 2010, but most surface O3 results are within the range. This difference may be related to the different simulation years, emission inventories, vertical and horizontal resolutions, and S-R revealing methods. Additional emission sensitivity simulation shows a negative O3 response in receptor region EA in January from EA. The difference between two methods in estimated S-R relationships of nss-sulfate and O3 are mainly due to ignoring the nonlinearity of pollutants during chemical processes. The S-R relationship of aerosols within EA subcontinent is also assessed. The model that we developed creates a link between the scientific community and policymakers. Finally, the results are discussed in the context of future model development and analysis opportunities.

New particle formation (NPF) is a significant source of atmospheric particles, affecting climate and air quality. Understanding the mechanisms involved in urban aerosols is important to develop effective mitigation strategies. However, NPF rates reported in the polluted boundary layer span more than 4 orders of magnitude, and the reasons behind this variability are the subject of intense scientific debate. Multiple atmospheric vapours have been postulated to participate in NPF, including sulfuric acid, ammonia, amines and organics, but their relative roles remain unclear. We investigated NPF in the CLOUD chamber using mixtures of anthropogenic vapours that simulate polluted boundary layer conditions. We demonstrate that NPF in polluted environments is largely driven by the formation of sulfuric acid–base clusters, stabilized by the presence of amines, high ammonia concentrations and lower temperatures. Aromatic oxidation products, despite their extremely low volatility, play a minor role in NPF in the chosen urban environment but can be important for particle growth and hence for the survival of newly formed particles. Our measurements quantitatively account for NPF in highly diverse urban environments and explain its large observed variability. Such quantitative information obtained under controlled laboratory conditions will help the interpretation of future ambient observations of NPF rates in polluted atmospheres.

Highly oxygenated organic molecules (HOMs) contribute substantially to the formation and growth of atmospheric aerosol particles, which affect air quality, human health and Earth's climate. HOMs are formed by rapid, gas-phase autoxidation of volatile organic compounds (VOCs) such as α-pinene, the most abundant monoterpene in the atmosphere. Due to their abundance and low volatility, HOMs can play an important role in new-particle formation (NPF) and the early growth of atmospheric aerosols, even without any further assistance of other low-volatility compounds such as sulfuric acid. Both the autoxidation reaction forming HOMs and their NPF rates are expected to be strongly dependent on temperature. However, experimental data on both effects are limited. Dedicated experiments were performed at the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber at CERN to address this question. In this study, we show that a decrease in temperature (from +25 to −50 ∘C) results in a reduced HOM yield and reduced oxidation state of the products, whereas the NPF rates (J1.7 nm) increase substantially. Measurements with two different chemical ionization mass spectrometers (using nitrate and protonated water as reagent ion, respectively) provide the molecular composition of the gaseous oxidation products, and a two-dimensional volatility basis set (2D VBS) model provides their volatility distribution. The HOM yield decreases with temperature from 6.2 % at 25 ∘C to 0.7 % at −50 ∘C. However, there is a strong reduction of the saturation vapor pressure of each oxidation state as the temperature is reduced. Overall, the reduction in volatility with temperature leads to an increase in the nucleation rates by up to 3 orders of magnitude at −50 ∘C compared with 25 ∘C. In addition, the enhancement of the nucleation rates by ions decreases with decreasing temperature, since the neutral molecular clusters have increased stability against evaporation. The resulting data quantify how the interplay between the temperature-dependent oxidation pathways and the associated vapor pressures affect biogenic NPF at the molecular level. Our measurements, therefore, improve our understanding of pure biogenic NPF for a wide range of tropospheric temperatures and precursor concentrations.

The extraction and timely updating of land use /cover information is a key issue in remote sensing change detection. The change vector analysis (CVA) is a better method of change detection. However, the CVA method is the blindness of artificial choice of threshold. Moreover, the direction cosine of CVA cannot represent the unique point in change vector space and it can’t distinguish the change category effectively. In order to avoid this defect, the midline vector is added to CVA method. In this paper, we use the midline change vector analysis (MCVA) method to detect the land use /cover change in multi temporal remote sensing images. We proposed the two-step threshold method to get the optimal threshold and determine the change and the unchanged region of the difference remote sensing image. We chose Hefei city of Anhui Province as the study area, and adopted two Landsat5 TM images in 2000 and 2008 year as experiment data. We use the MCVA and two-step threshold method to achieve remote sensing change detection. In order to compare the detection accuracy between MCVA method and the traditional post classification comparison method, the paper choose the same area (178 pixels × 180 pixels) in the two images to analyse the accuracy, and compare the accuracy of MCVA method with that of the traditional post classification comparison method based on SVM. The experiment results show that the MCVA method has higher overall accuracy, lower allocation disagreement and quantity disagreement. What’s more, the overall accuracy of MCVA method can reach nearly 60%, much higher than the traditional post classification comparison method (less than 40%). And the MCVA method can effectively avoid the problem of change vector direction cosine values are not unique, and the result is much better than the traditional post classification (SVM) comparison method. It indicates that MCVA is a more effective method in land use / cover change detection for middle resolution multispectral images.

In the present-day atmosphere, sulfuric acid is the most important vapour for aerosol particle formation and initial growth. However, the growth rates of nanoparticles ( nm) from sulfuric acid remain poorly measured. Therefore, the effect of stabilizing bases, the contribution of ions and the impact of attractive forces on molecular collisions are under debate. Here, we present precise growth rate measurements of uncharged sulfuric acid particles from 1.8 to 10 nm, performed under atmospheric conditions in the CERN (European Organization for Nuclear Research) CLOUD chamber. Our results show that the evaporation of sulfuric acid particles above 2 nm is negligible, and growth proceeds kinetically even at low ammonia concentrations. The experimental growth rates exceed the hard-sphere kinetic limit for the condensation of sulfuric acid. We demonstrate that this results from van der Waals forces between the vapour molecules and particles and disentangle it from charge–dipole interactions. The magnitude of the enhancement depends on the assumed particle hydration and collision kinetics but is increasingly important at smaller sizes, resulting in a steep rise in the observed growth rates with decreasing size. Including the experimental results in a global model, we find that the enhanced growth rate of sulfuric acid particles increases the predicted particle number concentrations in the upper free troposphere by more than 50 %.

Remote Sensing (RS) and Geography Information System (GIS) technologies are widely used in ecological analysis and regional planning. With the advantages of large scale monitoring, combination of point and area, multiple time-phases and repeated observation, they are suitable for monitoring and analysis of environmental information in a large range. In this study, support vector machine (SVM) classification algorithm is used to monitor the land use and land cover change (LUCC), and then to perform the ecological evaluation for Chaohu lake tourism area quantitatively. The automatic classification and the quantitative spatial-temporal analysis for the Chaohu Lake basin are realized by the analysis of multi-temporal and multispectral satellite images, DEM data and slope information data. Furthermore, the ecological buffer zone analysis is also studied to set up the buffer width for each catchment area surrounding Chaohu Lake. The results of LUCC monitoring from 1992 to 2015 has shown obvious affections by human activities. Since the construction of the Chaohu Lake basin is in the crucial stage of the rapid development of urbanization, the application of RS and GIS technique can effectively provide scientific basis for land use planning, ecological management, environmental protection and tourism resources development in the Chaohu Lake Basin.

This paper selected 2006-2016 MODIS NDVI data with a spatial resolution of 500m and time resolution of 16d, got the 11 years’ time series NDVI data of Maowusu sandy land through mosaicking, projection transformation, cutting process in batch. Analysed the spatial and temporal distribution and variation characteristics of vegetation cover in year, season and month time scales by maximum value composite, and unary linear regression analysis. Then, we combined the meteorological data of 33 sites around the sandy area, analysed the response characteristics of vegetation cover change to temperature and precipitation through Pearson correlation coefficient. Studies have shown that: (1) The NDVI value has a stable increase trend, which rate is 0.0075 / a. (2) The vegetation growth have significantly difference in four seasons, the NDVI value of summer > autumn > spring > winter. (3) The NDVI value change trend is conformed to the gauss normal distribution in a year, and it comes to be largest in August, its green season is in April, and yellow season is in the middle of November, the growth period is about 220 d. (4) The vegetation has a decreasing trend from the southeast to the northwest, most part is slightly improved, and Etuokeqianqi improved significantly. (5) The correlation indexes of annual NDVI with temperature and precipitation are −0.2178 and 0.6309, the vegetation growth is mainly affected by precipitation. In this study, a complete vegetation cover analysis and evaluation model for sandy land is established. It has important guiding significance for the sand ecological environment protection.

Clinically available agents that promote the suppression of inflammatory processes with minimal adverse effects are highly desired. An FDA-approved nutrition supplement, Intralipid®, has shown its immunomodulatory potential. However, its influence on organ transplant rejection has not been tested. Here, we have investigated whether Intralipid® affect allograft outcome in a fully major histocompatibility complex (MHC)-mismatched rat model of heterotopic working-heart transplantation. Recipients were treated intravenously with Intralipid® (1.5 g/kg, clinical dosage) or saline (untreated control) on post-transplantation days (PODs) 1, 3, and 6. Treatment with Intralipid® results in a significant prolongation of median graft survival from 7 days to 12 days. On POD 7, while the allografts from our untreated (i.e., control) group exhibit severe rejection, the allografts from Intralipid®-treated group show milder rejection demonstrating preserved myocardium integrity and decreasing infiltration of inflammatory cells (ED1+ and CD3+ cells). Flow cytometry analysis reveals a marked increase of CD4+CD25+Foxp3+ regulatory T-cells (Tregs) in the peripheral blood of Intralipid®-treated recipients beginning on POD 3 (7.0% vs. 14.4%, untreated vs. Intralipid®-treated). Intralipid® treatment also promotes Tregs in the spleens and allograft hearts. Toll-like receptor 4 expression is markedly suppressed in the Intralipid®-treated allografts. Our results suggest a new use of Intralipid® to modulate immune responses in the setting of heart transplantation. This study could stimulate further mechanistic as well as treatment studies leading to the clinical application of Intralipid® as an adjunctive therapy with current immunosuppression agents to manage organ rejection.

Using a formula for the total settlement of deep soil and the Winkler foundation model, the force model for underground pipelines is simplified to obtain the ultimate bending moments and to calculate the stress and strain due to double parallel shield tunneling construction. The effects of soil conditions, tunnel horizontal spacing L, pipeline material, pipeline depth h and soil loss ratioηl/ηf on the force on an underground pipeline are investigated. The results show that changing the soil conditions and value of L can have a significant effect on the shape and magnitude of the pipeline’s ultimate moment curve, although changing the value of h has less of an effect. It is found that the larger the bending stiffness of the pipeline, the larger the distribution range of the ultimate bending moment and its peak value, but there is less of an effect on the shape of the curve. Changes in the ratioηl/ηf have some influence on both the shape of the curve and the values of the bending stiffness of the pipeline.

Two series of donor-acceptor type conjugated polymers bearing benzotriazole or 5,6-dialkoxy-benzothiadiazole as acceptors unit are synthesized via Stille coupling. The optical, electrochemical, spectroelectrochemical characterizations and theoretical calculations are carried out. The polymers display excellent solubility in common organic solvents upon the introduction of long aliphatic side chains. The electrochromic performances of the polymers are also studied. Upon oxidation, the polymers switch from magenta or blue to a transmissive light blue state. In particular, respectable optical contrasts of about 44 and 40% are obtained in the visible and near infrared (NIR) region respectively for P2. The polymers also reveal fast switching of about from one second to a few seconds. In addition, P2 exhibits a high coloration efficiency of 518 and 561 cm2/C in the visible and NIR region respectively. The promising electrochromic behaviour of these polymers, together with the enhanced solubility will allow large-scale processing and advancement towards real applications.

The increase of population and economic development, especial the land use and urbanization bring the wetland resource a huge pressure and a serious consequence of a sharp drop in the recent years. Therefore wetland eco-environment degradation and sustainable development have become the focus of wetland research. Remote sensing technology has become an important means of environment dynamic monitoring. It has practical significance for wetland protection, restoration and sustainable utilization by using remote sensing technology to develop dynamic monitoring research of wetland spatial variation pattern. In view of the complexity of wetland information extraction performance of the SVM classifier, this paper proposes a feature weighted SVM classifier using mixed kernel function. In order to ensure the high-accuracy of the classification result, the feature spaces and the interpretation keys are constructed by the properties of different data. We use the GainRatio (featurei) to build the feature weighted parameter h and test the different kernel functions in SVM. Since the different kernel functions can influence fitting ability and prediction accuracy of SVM and the categories are more easily discriminated by the higher GainRatio, we introduce feature weighted ω calculated by GainRatio to the model. Accordingly we developed an improved model named "Feature weighted & Mixed kernel function SVM" based on a series of experiments. Taking the east beach of Chongming Island in Shanghai as case study, the improved model shows superiority of extensibility and stability in comparison with the classification results of the experiments applying the Minimum Distance classification, the Radial Basis Function of SVM classification and the Polynomial Kernel function of SVM classification with the use of Landsat TM data of 2009. This new model also avoids the weak correlation or uncorrelated characteristics' domination and integrates different information sources effectively to offer better mapping performance and more accurate result. The accuracy resulted from the improved model is better than others according to the Overall Accuracy, Kappa Coefficient, Omission Errors and Commission Errors.

A method is explored to remove tall building shadows in true color and color infrared urban aerial images based on the theory of color constancy. This paper first uses the specthem ratio and Otsu threshold segmentation methods to detect building shadows on urban aerial true color and color infrared aerial images. Then, based on the shadow detection result, one of the color constancy algorithms SoG (Shades of Gray) is used to remove the shadows in aerial images with different p values of the Minkowski norm. Finally, the shadow removal results with different p values have been compared by brightness, contrast and average gradients. The experiments show that the result of this method based on color constancy has a good visual effect, and different from general scene image shadow removal, the aerial images get the best shadow removal result when p is 2. It means the two types of aerial images should not be simply regarded as gray world images.