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For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step "coarse-to-fine registration" algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications. [ABSTRACT FROM AUTHOR]

Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing. [ABSTRACT FROM AUTHOR]

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China. [ABSTRACT FROM AUTHOR]

High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D) geo-positioning of the bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs) with respect to the high-resolution QuickBird stereo images in three spaces (i.e., orbital space, image space and object space). The compared models include a bias-corrected RSM in the orbital space, a bias-corrected RSM and RFM in the image space, and a bias-corrected RSM and RFM in the object space. In the comparison, the RSMs and RFMs use the vendor-provided orbit data and Rational Polynomial Coefficients (RPCs), respectively. The experimental results indicated that, (1) these five bias-corrected models can provide a sub-pixel geo-positioning accuracy. With the zero-order polynomial correction model in the orbital space and a minimum of three Ground Control Points (GCPs), the accuracy based on RPCs better than 0.8 m in horizontal direction and 1.3 m in vertical direction. With an increase in the number of GCPs, or in the order of correction models, the regenerated orbital parameters achieve a slight improved positioning accuracy of 0.5 m in horizontal direction and 0.8 m in vertical direction with 25 GCPs, which indicates that the low-order correction model in the orbital space can accurately model the effects of ephemeris and attitude errors; (2) the performances of bias-corrected RSM and RFM in image space are rather similar. However, the bias-corrected RSM and RFM in image space achieve a better accuracy than the bias-corrected RSM and RFM in object space, with the same configuration of GCPs. [ABSTRACT FROM AUTHOR]

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings' mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture. [ABSTRACT FROM AUTHOR]

Natural wetland ecosystems provide not only important habitats for many wildlife species, but also food for migratory and resident animals. In Shanghai, the Chongming Dongtan International Wetland, located at the mouth of the Yangtze River, plays an important role in maintaining both ecosystem health and ecological security of the island. Meanwhile it provides an especially important stopover and overwintering site for migratory birds, being located in the middle of the East Asian-Australasian Flyway. However, with the increase in development intensity and human activities, this wetland suffers from increasing environmental pressure. On the other hand, biological succession in the mudflat wetland makes Chongming Dongtan a rapidly developing and rare ecosystem in the world. Therefore, studying the wetland spatio-temporal change is an important precondition for analyzing the relationship between wetland evolution processes and human activities. This paper presents a novel method for analyzing land-use/cover changes (LUCC) on Chongming Dongtan wetland using multispectral satellite images. Our method mainly takes advantages of a machine learning algorithm, named the Kernel Extreme Learning Machine (K-ELM), which is applied to distinguish between different objects and extract their information from images. In the K-ELM, the kernel trick makes it more stable and accurate. The comparison between K-ELM and three other conventional classification methods indicates that the proposed K-ELM has the highest overall accuracy, especially for distinguishing between Spartina alternflora, Scirpus mariqueter, and Phragmites australis. Meanwhile, its efficiency is remarkable as well. Then a total of eight Landsat TM series images acquired from 1986 to 2013 were used for the LUCC analysis with K-ELM. According to the classification result, the change detection and spatio-temporal quantitative analysis were performed. The specific analysis of different objects are significant for learning about the historical changes to Chongming Dongtan and obtaining the evaluation rules. Generally, the rapid speed of Chongming Dongtan's urbanization brought about great influence with respect to natural resources and the environment. Integrating the results into the ecological analysis and ecological regional planning of Dongtan could provide a reliable scientific basis for rational planning, development, and the ecological balance and regional sustainability of the wetland area. [ABSTRACT FROM AUTHOR]