- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas
摘要： Existing semantic segmentation models of urban areas have shown to perform well in a supervised setting. However, collecting lots of annotated images from each city to train such models is time-consuming or difficult. In addition, when transferring the segmentation model from the trained city (source domain) to an unseen city (target domain), the performance will largely degrade due to the domain shift. For this reason, we propose a domain adaptation method with a domain similarity discriminator to eliminate such domain shift in the framework of adversarial learning. Contrary to the single-input adversarial network, our domain similarity discriminator, which consists of a Siamese network, is able to measure the similarity of the pairwise-input data. In this way, we can use more information about the pairwise-input to measure the similarity between different distributions so as to address the problem of domain shift. Experimental results demonstrate that our approach outperforms the competing methods on three different cities.
关键词： domain adaptation,urban areas,semantic segmentation,domain shift,Siamese network
更新于2025-09-23 15:22:29
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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Instance Segmentation of Trees in Urban Areas from MLS Point Clouds Using Supervoxel Contexts and Graph-Based Optimization
摘要： In this paper, an instance segmentation method for tree extraction from MLS data sets in urban scenes is developed. The proposed method utilizes a supervoxel structure to organize the point clouds, and then extracts the detrended geometric features from the local context of supervoxels. Combined with the detrended features of the local context, the Random Forest (RF) classifier will be adopted to obtain the initial semantic labeling results of trees from point clouds. Afterwards, a local context-based regularization is iteratively performed to achieve global optimum on a global graphical model, in order to spatially smoothing the semantic labeling results. Finally, a graph-based segmentation is conducted to separate individual trees according to the semantic labeling results. The use of supervoxel structure can preserve the geometric boundaries of objects in the scene, and compared with point-based solutions, the supervoxel-based method can largely decrease the number of basic elements during the processing. Besides, the introduction of supervoxel contexts can extract the local information of an object making the feature extraction more robust and representative. Detrended geometric features can get over the redundant and in-salient information in the local context, so that discriminative features are obtained. Benefiting from the regularization process, the spatial smoothing is obtained based on initial labeling results from classic classifications such as RF classification. As a result, misclassification errors are removed to a large degree and semantic labeling results are thus smoothed. Based on the constructed global graphical model during the spatially smoothing process, a graph-based segmentation is applied to partition the graphical model for the clustering the instances of trees. The experiments on two test datasets have shown promising results, with an accuracy of the semantic labeling of trees reaching around 0.9. The segmentation of trees using graph-based algorithm also show acceptable results, with trees having simple structures and sparse distributions correctly separated, but for those cramped trees with complex structures, the points are over- or under-segmented.
关键词： local context,urban areas,supervoxels,MLS,Instance segmentation,graph-based segmentation,trees
更新于2025-09-23 15:22:29
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Remote Sensing-Based Urban Land Use/Land Cover Change Detection and Monitoring
摘要： Recently, the pull of urban center is snowballing, more and more people move to the urban center to earn more money than rural areas especially in developing countries. Consequently, as more people arrive at urban areas, the more pressure will be on the urban environment. Population growth, immigration, and growing environmental problems entail advanced systems for city planners to help sustainable development in these rapidly changing regions. This problem can solve using remote sensing system. The purpose of this paper is to reveal the application of remote sensing technology for urban and use and land cover change detection and disclose certain sequences in detecting urban land use change.
关键词： Change detection,Remote sensing,Urban areas,Land cover change
更新于2025-09-19 17:15:36
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Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data
摘要： High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature.
关键词： photovoltaic panels,detection and area estimation,urban areas,hyperspectral unmixing,hyperspectral imaging,partial nonnegative matrix factorization
更新于2025-09-16 10:30:52
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Photovoltaic systems with vertically mounted bifacial PV modules in combination with green roofs
摘要： Dependent on the specific conditions flat roofs can be well suited for the installation of large photovoltaic systems in urban areas. For urban designers also other aspects, such as the insulation of buildings, cooling, air purification and water retention play an important role besides the ecological energy generation. The combination of photovoltaics and roof greening can therefore be an interesting fusion. It combines the advantages of a green roof with the local electrical energy production at the place of consumption. However, using a conventional photovoltaic system with tilted modules in south or east-west direction on a green roof causes problems, as typical low tilt angels and high ground coverage rates result in an almost complete coverage of the roof surface. Plants, growing in between the covered areas provoke undesirable shading of the collector surface. Only a frequent maintenance procedure, complicated by dense PV system layouts, can avoid a reduction of the energy yield in the course of time. Vertically mounted specially designed bifacial modules are an option to realize photovoltaic power generation in combination with a functional green roof at low maintenance costs. In this paper, we report on the layout and the energy yield of a corresponding system. Custom-made bifacial modules with 20 cells were produced and vertically installed in landscape orientation. The narrow layout of the modules lowers the wind load and reduces the visibility. The enhanced power in the morning and evening of vertically east-west installed modules can additionally lead to higher self-consumptions rates. Despite having some shading and undergrounds with albedo factors of less than 0.2, the bifacial installation with a rated power of 9.09 kWp achieved a specific yield of the 942 kWh/kWp in one year (11.08.2017–10.08.2018). This is close to typical values of 1000 kWh/kWp achieved for south-facing PV systems in the same region. The impact of the greening on the albedo and the system performance is investigated in more detail with two smaller sub-systems. The energy yields of the two bifacial sub-systems are compared to a monofacial, south-facing reference module. The use of silver-leaved plants in this system resulted in higher albedo values and a more resilient roof greening.
关键词： PV system,Bifacial,Albedo,Urban areas,Vertical,Green roof
更新于2025-09-12 10:27:22
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Extracting Accurate Building Information from Off-Nadir VHR Images
摘要： This research demonstrates the applicability of the improved algorithm for generating LoS-DSM elevation data through an elevation-based building detection in off-nadir VHR satellite imagery acquired over a dense urban area. The improved LoS-DSM algorithm was executed over a test dataset. The achieved image-elevation co-registration was very successful based on a visual assessment. Then, the generated and co-registered elevation data were applied in elevation-based building detection. The achieved building map was enhanced based on vegetation and occlusion masks as well as some morphological operations. The quality of the detection was evaluated based on manually generated reference data. The overall detection quality was found to be more than 90% with almost 95% of complete and correct detection. This level of performance in such a challenging dense urban area proves the high success of the disparity-based image-data co-registration as well as the applicability of the developed LoS-DSM elevations to detecting building objects even in off-nadir VHR satellite images acquired over dense urban areas.
关键词： off-nadir VHR images,urban areas,building detection,LoS-DSM,image-elevation co-registration
更新于2025-09-11 14:15:04
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Supervised spatial classification of multispectral LiDAR data in urban areas
摘要： Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas. The study area is located in Oshawa, Ontario, Canada, on the Lake Ontario shoreline. Multispectral Optech Titan LiDAR data over the study area were acquired on 3 September 2014 in a single strip of 3 km2. Using the channels at 1,550 nm (C1), 1,064 nm (C2) and 532 nm (C3), LiDAR intensity data, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and a novel hierarchical morphological profiles (HMP) were derived and used as features for the classification. A support vector machine classifier with a radial basis function (RBF) kernel was applied in the classification stage, where the optimal parameters for the classifier were selected by a grid search procedure. The combination of intensity, pseudoNDVI, nDSM and HMP resulted in the best land cover classification, with an overall accuracy of 93.28%.
关键词： land cover classification,urban areas,morphological profiles,spatial classification,multispectral LiDAR
更新于2025-09-11 14:15:04
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Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery
摘要： Luojia 1-01 satellite, launched on 2 June 2018, provides a new data source of nighttime light at 130 m resolution and shows potential for mapping urban extent. In this paper, using Luojia 1-01 and VIIRS nighttime light imagery, we compared several methods for extracting urban areas, including Human Settlement Index (HSI), Simple Thresholding Segmentation (STS) and SVM supervised classification. According to the accuracy assessment, the HSI method using LJ1-01 data had the best performance in urban extent extraction, which presented the largest Kappa Coefficient value, 0.834, among all the results. For the urban areas extracted by VIIRS based HSI method, the largest Kappa Coefficient value was 0.772. In contrast, the largest Kappa Coefficient values obtained by STS method were 0.79 and 0.7512 respectively when using LJ1-01 and VIIRS data, while for SVM method the values were 0.7829 and 0.7486 when using Landsat-LJ and Landsat-VIIRS composite data respectively. The experimented results demonstrated that the utilization of nighttime light imagery can largely improve the accuracy of urban extent extraction and LJ1-01 data, with a higher resolution and more abundant spatial information, can lead to better identification results than its predecessors.
关键词： VIIRS DNB,urban areas,LJ1-01 data,nighttime light imagery,human settlement index
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Worldpop - Fusion of Earth and Big Data for Intraurban Population Mapping
摘要： High resolution estimates of human population distributions are very useful for large-scale or national scale analyses in many fields including epidemiology, healthcare, resource distribution, and development. Population densities have long been estimated using remote sensing data, particularly at large spatial scales. However, the accuracy of population density predictions can be very poor in cities, and this is particularly relevant in urban areas in sub-Saharan Africa. Here we map intra-urban population densities for select African cities by disaggregating census data using random forest techniques with remotely-sensed and geospatial data, including bespoke time-series intra-urban built-up data. We produce maps with up to 83% explained variance and find including built-up density layers in urban population models allows for clear improvements in prediction.
关键词： machine learning,population density,census,built-up,Urban areas,Africa
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - The “Urban Geomatics for Bulk Information Generation, Data Assessment and Technology Awareness” Project: Detection, Representation and Analysis of the Urban Scenario Changes
摘要： About 54% of world population nowadays lives in urban areas and this percentage is expected to increase up to 66% by 2050. Therefore, it is crucial to manage this social and cultural change by collecting, integrating and sharing reliable and open spatial information concerning the urban environments where we leave in. The present availability of huge archives of synthetic aperture radar (SAR) data collected since 1992 by ESA, in conjunction with the available Earth-Observation (EO) optical data, represents a unique possibility to derive valuable information for the understanding of the ongoing urban processes. In this framework, the three-year project financed by the Italian Ministry of Instruction, Research and University, entitled “URBAN GEOmatics for bulk information generation, data assessment and technology awareness” may play a role for the assessment and the development of new replicable methodologies for the study of soil consumption and mobility in urban zones. The paper aims to present some preliminary results achieved during the project, clarifying how the new emerging technologies for managing big EO data are proficient for the investigation of urban processes.
关键词： InSAR,Urban areas,soil consumption
更新于2025-09-10 09:29:36