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Nighttime light pollution mars the view of the night sky, wastes energy, and adversely affects ecosystems and human health. In environmental design, efforts to reduce light pollution have often focused on the use of cutoff light f...
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Nighttime light pollution mars the view of the night sky, wastes energy, and adversely affects ecosystems and human health. In environmental design, efforts to reduce light pollution have often focused on the use of cutoff light fixtures to direct light toward the ground rather than skyward. The results of this study, however, indicate that light-fixture-oriented solutions are not a sufficient response to the problem. The study examined the relationship between nighttime light pollution and land-use types and found that some land-use types and their associated ground materials produce reflected light that contributes to light-pollution levels. As a first step to studying the relationship of land-use types to light pollution, a new method of acquiring high-resolution (sub-30-cm resolution) nighttime aerial images was developed to depict the reflected brightness of artificial lighting with a high degree of accuracy. Once the high-resolution imagery was acquired, 11 land-use types were delineated in the study area based on inspection of daytime aerial images and site visits. Finally, land uses and the nighttime image were compared, and an association between land-use types and nighttime light pollution levels was established. The general finding is that land-use types associated with highly reflective materials, such as concrete parking structures, reflect large amounts of light, producing light pollution even when full cutoff lights are used.
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The adoption of rational functions as a preferred sensor orientation model for narrow field of view line scanner imagery accompanied the introduction of commercial high-resolution satellite imagery (HRSI) at the turn of the millen...
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The adoption of rational functions as a preferred sensor orientation model for narrow field of view line scanner imagery accompanied the introduction of commercial high-resolution satellite imagery (HRSI) at the turn of the millennium. This paper reviews the developments in ground point determination from HRSI via the model of terrain independent rational polynomial coefficients (RPCs). A brief mathematical background to rational functions is first presented, along with a review of the models for generating RPCs from a rigorous sensor orientation, and for geopositioning via either forward intersection or monoplotting. The concept of RPC block adjustment with compensation for exterior orientation biases is then discussed, as is the means to enhance the original RPCs through a bias correction procedure. The potential for RPC block adjustment to yield sub-pixel geopositioning accuracy from HRSI is illustrated using results from experimental testing with two Quickbird stereo image pairs and three multi-image IKONOS blocks. Finally, error propagation issues in RPC block adjustment of HRSI are considered.
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Shorelines are line features that mark the interface between water and land and they are essential due to their mapping, ecological, and engineering values. A shoreline can be an instantaneous, which shows the water-land boundary ...
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Shorelines are line features that mark the interface between water and land and they are essential due to their mapping, ecological, and engineering values. A shoreline can be an instantaneous, which shows the water-land boundary at any time or a tide-referenced, which indicates the intersection of a tidal datum surface such as mean high water and the coastal topobathymetric surface. Instantaneous shorelines can be extracted from geo-referenced aerial photographs and satellite imagery without the need to have its acquisition to be tide-coordinated as in the case of tide-referenced shorelines. In this study, a semiautomatic method of extracting the shoreline from high-resolution satellite imagery and a coastal terrain model (CTM) is presented. This study was motivated by the frequent need for shorelines for input in the numerical models used by Dubai Municipality for obtaining essential parameters for various coastal planning activities. The method utilizes the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) for classifying a RapidEye image of the study area, then vectorizing and extracting the shoreline, and finally extracting the elevation of shoreline points from the CTM and adding that to the shoreline point attribute table. The accuracy of the extracted shoreline was carried out by comparing it to a shoreline digitized from an orthophotograph of the study area. The comparison was carried out by computing the point-to-point distances between corresponding points established on the two shorelines using a parametric technique. The results suggest that this method can be used to accurately extract robust shorelines from high-resolution satellite imagery and CTMs.
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In olive, both discontinuous canopy (open vase configuration) and continuous canopy (hedgerow) systems are used in commercial planting. Reliable and cost effective plant architecture characterization is necessary to test the suita...
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In olive, both discontinuous canopy (open vase configuration) and continuous canopy (hedgerow) systems are used in commercial planting. Reliable and cost effective plant architecture characterization is necessary to test the suitability of cultivars to specific growing systems, particularly in olive breeding programs where a large number of genotypes must be evaluated. In this work, the performance of a method for the estimation of olive tree height and crow diameter based on low cost unmanned aerialvehicle (UAV) imagery was evaluated. Selections from breeding programs cultivated either on discontinuous or continuous canopy cropping systems were used for developing the models. Averaged data by genotypes showed significant linear fits between reference field measurements and remote sensing estimation of crown parameters with R2 values of 0.89 for height estimation in hedgerow plantations, 0.66 and 0.53 for crown diameter in discontinuous canopy and continuous hedgerows respectively, but only 0.14 between measured and estimated height in discontinuous canopy genotypes. These results indicate that low cost UAV imagery could be used for evaluating olive tree crown parameters (tree height and crown diameter) in olive breeding programs, providing accurate enough results for selection of genotypes.
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In vegetation change monitoring and urban planning, the measurement and mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular measure to genera...
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In vegetation change monitoring and urban planning, the measurement and mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular measure to generate vegetation maps which resolution depends on that of the input images. High-resolution imagery can lead to better vegetation classification accuracy. Various methods are proposed to provide high spatial resolution vegetation indices based on a fusion concept. IKONOS produces high spatial resolution panchromatic (Pan) images and moderate spatial resolution multispectral (MS) images. Generally, for an image fusion purpose, the conventional bi-cubic interpolation scheme is used to resize MS images. Nevertheless, this scheme fails around edges and consequently produces blurred edges and annoying artifacts in interpolated MS images. To avoid this problem, an artifact-free image interpolation method is proposed. This study presents a modified NDVI that provides high spatial resolution maps which differentiate vegetated surfaces from other surfaces when using IKONOS imagery. This vegetation index (HRNDVI: high resolution NDVI) is based on a newly derived formula including high spatial resolution information from IKONOS. The HRNDVI is computed based on the resampled MS images and the Pan images. The proposed vegetation index takes advantage of both the high spatial resolution information of Pan images and the robustness of the interpolation technique. Visual and quantitative analysis demonstrates that this index appears promising and performs well in vegetation extraction and visualization.
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This paper studies the problem of the polygonal mapping of buildings by tackling the issue of mask reversibility, which leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. W...
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This paper studies the problem of the polygonal mapping of buildings by tackling the issue of mask reversibility, which leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments, and high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on four public benchmarks, including the AICrowd, Open Cities, Shanghai, and Inria datasets. On the AICrowd, Open Cities, and Shanghai datasets, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS by large margins. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU/HiSup.
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The growing applications of digital surface models (DSMs) for object detection, segmentation and representation of terrestrial landscapes have provided impetus for further automation of 3D spatial information extraction processes....
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The growing applications of digital surface models (DSMs) for object detection, segmentation and representation of terrestrial landscapes have provided impetus for further automation of 3D spatial information extraction processes. While new technologies such as lidar are available for almost instant DSM generation, the use of stereoscopic high-resolution satellite imagery (HRSI), coupled with image matching, affords cost-effective measurement of surface topography over large coverage areas. This investigation explores the potential of IKONOS Geo stereo imagery for producing DSMs using an alternative sensor orientation model, namely bias-corrected rational polynomial coefficients (RPCs), and a hybrid image-matching algorithm. To serve both as a reference surface and a basis for comparison, a lidar DSM was employed in the Hobart testfield, a region of differing terrain types and slope. In order to take topographic variation within the modelled surface into account, the lidar strip was divided into separate sub-areas representing differing land cover types. It is shown that over topographically diverse areas, heighting accuracy to better than 3 pixels can be readily achieved. Results improve markedly in feature-rich open and relatively flat terrain, with sub-pixel accuracy being achieved at check points surveyed using the global positioning system (GPS). This assessment demonstrates that the outlook for DSM generation from HRSI is very promising.
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We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imag...
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We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imagery with lower spatial resolutions down to 30 m. We classified forest type using spectral information alone, texture information alone, and spectral and texture information combined at each spatial resolution, and compared the classification accuracy at each resolution. The classification and regression tree method was used for classification. The accuracy of all three tests decreased slightly with lower spatial resolution. The accuracy with the combined data was generally higher than with either the spectral or texture information alone. At most resolutions, the lowest accuracy was with texture information alone. However, there was no clear difference in accuracy between the combined data and spectral data alone at 25- and 30-m spatial resolution. These results indicate that adding texture information to spatial information improves the accuracy of forest type classification from very high resolution (4-m spatial resolution) to medium resolution imagery (20-m spatial resolution), but this accuracy improvement does not appear to hold for relatively coarse resolution imagery (25- to 30-m spatial resolution).
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We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imag...
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We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imagery with lower spatial resolutions down to 30 m. We classified forest type using spectral information alone, texture information alone, and spectral and texture information combined at each spatial resolution, and compared the classification accuracy at each resolution. The classification and regression tree method was used for classification. The accuracy of all three tests decreased slightly with lower spatial resolution. The accuracy with the combined data was generally higher than with either the spectral or texture information alone. At most resolutions, the lowest accuracy was with texture information alone. However, there was no clear difference in accuracy between the combined data and spectral data alone at 25- and 30-m spatial resolution. These results indicate that adding texture information to spatial information improves the accuracy of forest type classification from very high resolution (4-m spatial resolution) to medium resolution imagery (20-m spatial resolution), but this accuracy improvement does not appear to hold for relatively coarse resolution imagery (25- to 30-m spatial resolution).
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As a model for sensor orientation and 3D geopositioning for high-resolution satellite imagery (HRSI), the affine transformation from object to image space has obvious advantages. Chief among these is that it is a straightforward l...
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As a model for sensor orientation and 3D geopositioning for high-resolution satellite imagery (HRSI), the affine transformation from object to image space has obvious advantages. Chief among these is that it is a straightforward linear model, comprising only eight parameters, which has been shown to yield sub-pixel geopositioning accuracy when applied to Ikonos stereo imagery. This paper aims to provide further insight into the affine model in order to understand why it performs as well as it does. Initially, the model is compared to counterpart, 'rigorous' affine transformation formulations which account for the conversion from a central perspective to affine image. Examination of these rigorous models sheds light on issues such as the effects of terrain and size of area, as well as upon the choice of reference coordinate system and the impact of the adopted scanning mode of the sensor. The results of application of the affine sensor orientation model to four multi-image Ikonos test field configurations are then presented. These illustrate the very high geopositioning accuracy attainable with the affine model, and illustrate that the model is not affected by size of area, but can be influenced to a modest extent by mountainous terrain, the mode of scanning and the choice of object space coordinate system. Above all, the affine model is shown to be both a robust and practical sensor orientation/triangulation model with high metric potential.
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