Exploitation Of Imagery Research Articles

Underwater 3D Reconstruction from Video or Still Imagery: Matisse and 3DMetrics Processing and Exploitation Software

This paper addresses the lack of “push-button” software for optical marine imaging, which currently limits the use of photogrammetric approaches by a wider community. It presents and reviews an open source software, Matisse, for creating textured 3D models of complex underwater scenes from video or still images. This software, developed for non-experts, enables routine and efficient processing of underwater images into 3D models that facilitate the exploitation and analysis of underwater imagery. When vehicle navigation data are available, Matisse allows for seamless integration of such data to produce 3D reconstructions that are georeferenced and properly scaled. The software includes pre-processing tools to extract images from videos and to make corrections for color and uneven lighting. Four datasets of different 3D scenes are provided for demonstration. They include both input images and navigation and associated 3D models generated with Matisse. The datasets, captured under different survey geometries, lead to 3D models of different sizes and demonstrate the capabilities of the software. The software suite also includes a 3D scene analysis tool, 3DMetrics, which can be used to visualize 3D scenes, incorporate elevation terrain models (e.g., from high-resolution bathymetry data) and manage, extract, and export quantitative measurements for the 3D data analysis. Both software packages are publicly available.

Structural relationships of the younger granites of Tirmini (Damagaram south-east, Niger)

The Tirmini anorogenic ring complex is located approximately 25 km west of the town of Zinder, in the pan-African province of Damagaram-Mounio, the southeastern terminus of the Benin-Nigeria Shield. The aim of this study is to characterise the anorogenic deformation affecting the Tirmini younger granites. The petrography of Tirmini evolves from rhyolites to microgranites, granites and quartz alkaline syenite emplaced during anorogenic magmatism in the Permian (295 Ma). Very little structural data is available on the Tirmini complex. Only a cursory study of deformation has been carried out, without characterisation of the stages of anorogenic deformation or structural interpretation. The aim of this study is to characterise the structural evolution of the Tirmini younger granites. The methodology used is based on the exploitation of satellite imagery and mapping, supported by an analysis of deformation carried out in the field using Canvas software. The geological structures revealed include 360° schistosities, fracture schistosities, detachments (dexter and sinistral) anµ diaclases. The rheology of these structures has revealed two chronologically marked stages of anorogenic deformation (i) semi-ductile to brittle, Sd1, and (ii) frankly brittle, Sd2. The first stage of deformation is contemporaneous with magmatic activity, producing 360° schistosities linked to magma swelling and fracture schistosities (S1: N45°-N60° and S2: N75°-N95°) associated with the emplacement of granites and syenite. The second brittle stage, Sd2, is characterised by a system of conjugate dextral and sinistral detachments and two families of diaclases (F1: N45°-N60° and F2: N120°-N150°) associated with the emplacement of the pluton and the cooling of the magma.

Field hyperspectral data and OLI8 multispectral imagery for heavy metal content prediction and mapping around an abandoned Pb–Zn mining site in northern Tunisia

Mining and smelting releases toxic contaminants such as zinc (Zn), lead (Pb) or cadmium (Cd) into the soil thereby poisoning it and rendering it unproductive. Remotely alternatives have been widely employed in the attempt of estimating heavy metal content within soils. The present study provides a methodological approach based on VNIR-SWIR field hyperspectral data and multispectral Landsat OLI 8 imageries for the prediction and mapping of Pb, Zn and Cd heavy metal contents around the abandoned Jebel Ressas mine site in Northern Tunisia. Thus, eighty-seven soil and tailing samples were collected from the study site and VNIR-SWIR field reflectances were measured on the same collection points, as well. All samples were analysed by atomic absorption for the estimation of heavy metal concentrations. The partial least squares regression PLSR was conducted considering the measured heavy metal concentrations and using multi-scale data: VNIR-SWIR field hyperspectral data and multispectral Landsat OLI 8 imagery. Standard normal variable (SNV) and multiple scatter correction (MSC) preprocessing methods were applied for further mapping improvement. Thus, this work aims to automate the estimation of the heavy metal contents in contaminated soils, by carrying out: a modeling approach based on the PLSR using VNIR-SWIR field hyperspectral data, ii) the mapping of Pb and Zn contents thanks to the exploitation of Landsat OLI8 multispectral imagery and iii) the application of both MSC and SNV preprocessing methods to optimize the performance of the developed models, when using such spectrally and spatially degraded data.

Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.

Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data

In this paper, we establish a workflow for estimation of built-up density and height based on multispectral Sentinel-2 data. To do so, we render the estimation of built-up density and height as a supervised learning problem. Given the rational level of measurement of those two target variables, the regression estimation problem is regarded as finding the mapping between an incoming vector, i.e., ubiquitously available features computed from Sentinel-2 data, and an observable output (i.e., training set), which is derived over spatially limited areas in an automated manner. As such, training sets are automatically generated from a joint exploitation of TanDEM-X mission elevation data and Sentinel-2 imagery, and, as an alternative, from cadastral sources. The training sets are used to regress the target variables for spatial processing units which correspond to urban neighborhood scales. From a methodological point of view, we introduce a novel ensemble regression approach, i.e., multistrategy ensemble regression (MSER), based on advanced machine learning-based regression algorithms including Random Forest Regression, Support Vector Regression, Gaussian Process Regression, and Neural Network Regression. To establish a robust ensemble, those algorithms are learned with a modified version of the AdaBoost.RT algorithm. However, to reliably ensure diversity between single boosted regressors, we include a random feature subspace method in the procedure. In contrast to existing approaches, we selectively prune non-favorable regressors trained during the boosting procedure and calculate the final prediction by a weighted mean function on the residual models to ensure enhanced accuracy properties of predictions. Finally, outputs are concatenated into a single prediction with a decision fusion strategy. Experimental results are obtained from four test areas which cover the settlement areas of the four largest German cites, i.e., Berlin, Hamburg, Munich, and Cologne. The results unambiguously underline the beneficial properties of the MSER approach, since all best predictions were obtained with a boosted regressor in conjunction with a decision fusion strategy in a comparative setup. The mean absolute errors of corresponding models vary between 3 and 16% and 1–5.4 m with respect to built-up density and height, respectively, depending on the validation strategy, size of the spatial processing units, and test area. Also in a domain adaptation setup (i.e., when learning a model over a source domain and applying it over a geographically different target domain) numerous predictions show comparable accuracy levels as predictions obtained within a source domain. This further underlines the viability to transfer a model and, thus, enable a substitution of the training data in the target domains.

Evaluation of Rapeseed Winter Crop Damage Using UAV-Based Multispectral Imagery

This research is related to the exploitation of multispectral imagery from an unmanned aerial vehicle (UAV) in the assessment of damage to rapeseed after winter. Such damage is one of a few cases for which reimbursement may be claimed in agricultural insurance. Since direct measurements are difficult in such a case, mainly because of large, unreachable areas, it is therefore important to be able to use remote sensing in the assessment of the plant surface affected by frost damage. In this experiment, UAV images were taken using a Sequoia multispectral camera that collected data in four spectral bands: green, red, red-edge, and near-infrared. Data were acquired from three altitudes above the ground, which resulted in different ground sampling distances. Within several tests, various vegetation indices, calculated based on four spectral bands, were used in the experiment (normalized difference vegetation index (NDVI), normalized difference vegetation index—red edge (NDVI_RE), optimized soil adjusted vegetation index (OSAVI), optimized soil adjusted vegetation index—red edge (OSAVI_RE), soil adjusted vegetation index (SAVI), soil adjusted vegetation index—red edge (SAVI_RE)). As a result, selected vegetation indices were provided to classify the areas which qualified for reimbursement due to frost damage. The negative influence of visible technical roads was proved and eliminated using OBIA (object-based image analysis) to select and remove roads from classified images selected for classification. Detection of damaged areas was performed using three different approaches, one object-based and two pixel-based. Different ground sampling distances and different vegetation indices were tested within the experiment, which demonstrated the possibility of using the modern low-altitude photogrammetry of a UAV platform with a multispectral sensor in applications related to agriculture. Within the tests performed, it was shown that detection using UAV-based multispectral data can be a successful alternative for direct measurements in a field to estimate the area of winterkill damage. The best results were achieved in the study of damage detection using OSAVI and NDVI and images with ground sampling distance (GSD) = 10 cm, with an overall classification accuracy of 95% and a F1-score value of 0.87. Other results of approaches with different flight settings and vegetation indices were also promising.

CALIBRATION OF THE SSOT MISSION USING A VICARIOUS APPROACH BASED ON OBSERVATIONS OVER THE ATACAMA DESERT AND THE GOBABEB RADCALNET STATION

Abstract. This work presents the results of the absolute radiometric calibration of the sensor on-board the “Sistema Satelital de Observación de la Tierra” (SSOT) using the vicarious approach based on in-situ measurements of surface reflectance and atmospheric retrievals. The SSOT mission, also known as FASat-Charlie, has been successfully operating for almost nine years ‒at the time of writing‒, exceeding its five-year nominal design life and providing multispectral and panchromatic imagery for different applications. The data acquired by SSOT has been used for emergency and disaster management and monitoring, cadastral mapping, urban planning, defense purposes, among other uses. In this paper, some results of the efforts conducting to the exploitation of the SSOT imagery for remote sensing quantitative applications are detailed. The results of the assessment of the radiometric calibration of the satellite sensor, performed in the Atacama Desert, Chile, using the data acquired and made available by the Gobabeb Station of Radiometric Calibration Network (RadCalNet), Namibia, are presented. Additionally, we describe the process for obtaining the absolute gains for the multispectral and panchromatic bands of the SSOT sensor by adapting the reflectance−based approach (Thome et al., 2001). The outputs achieved from the Atacama data collection have generated consistent results and average differences in the order of 3% with respect to the RadCalNet TOA reflectances. The presented results are an example of the benefits of having access to the RadCalNet data and how it increases the opportunity of conducting Cal/Val activities using endorsed calibration sites.

Application of remote sensing techniques in lithologic mapping of Djanet Region, Eastern Hoggar Shield, Algeria

Application of remotely sensed data in geological mapping has been the subject matter of many studies in the last decades. The optimal exploitation of high spatial resolution satellite imagery can contribute to the improvement of the geological map, particularly when it comes to mapping in arid and desert areas where outcrops are often inaccessible. Djanet area lies in Eastern Hoggar Shield, Algeria. It is characterized by rather high corrugated reliefs and this makes it difficult to collect samples or even get field information from the entire region. The study covers the terrane of Djanet and part of the terrane of Edembo. In addition, this region is probably one of the least explored areas of Hoggar. This is why different remote sensing techniques have been used to make geologic mapping faster and more efficient. Processing of Landsat-7 ETM+ images covering the Djanet region includes various treatments ranging from contrast enhancements to spectral enhancements, and using images in 742 RGB color compositions, calculation processing of band ratios (3/1, 5/4, 7/5) and the (5/7, 2/1, 4/2) and the application of the directional filters, according to the orientations ranging from N000 up to N180E, with a semi-automatic approach for the extraction of lineaments to establish two maps, a lithological map and another linear map. The correlation of these maps with the field data allowed us to check the validity and the correspondence of the different facies and especially to clarify the lithological contours and the identification of the majority of the tectonic accidents derived from the analysis of Landsat satellite data. The geological map obtained gives another image of the region studied and provides new information on the contacts of the lithological units and the structural diagram.

Connotative Confluence: Imagery and its functions in Shakespeare’s King Lear

Written in the early 1600s, King Lear, an early modern tragedy with the human condition as its main premise, displays Shakespeare’s effective exploitation of complex imagery. Through various images and extended or long drawn out metaphors, Shakespeare not only comments on character, plot, action, man’s position in the universe in relation to Nature, offspring and siblings, but also addresses such questions as political legitimacy, treason, treachery, aristocracy and the relationship between land and the monarch. In a turbulent period marked by strict rules against commenting directly on politics and royalty even in the parliament, imagery also serves as advice for the monarch in the tradition of speculum principis i.e., mirror for princes literature. This paper discusses the effect and manifold functions of various imagistic techniques used in King Lear and how imagery as a stylistic tool helps the playwright to substantially expand the meanings of the play making it a timeless and universal reading not only for the learners of Literature, but also for historians, psychologists, political scientists, philosophers, economists and food theorists, to mention only a few.

Dense Stereo Matching With Edge-Constrained Penalty Tuning

Dense stereo matching has been an indispensable technique for 3-D exploitation of imagery, in which semiglobal matching (SGM) is one of the outstanding approaches and has numerous variations in the literature. However, generating accurate disparity in depth discontinuity areas remains challenging, since the cost-aggregation step of SGM providing consistency constraints is sensitive to nonoptimal parametrizations of relevant penalty functions. In this letter, we present a gradual SGM cost aggregation that comprises a penalty tuning process and involves edge feature knowledge. To be more specific, we propose an additional penalty parameter and a weighting formula to handle edge pixels with depth variations, acquiring satisfactory depth estimation by preserving sharp geometric edges and maintaining smoothness without raising extra noise. Evaluations on well-known benchmark and real large-scale images are promising, and the latter case yields point clouds with sharp edges and smooth surfaces showing the effectiveness and feasibility of the proposed method.

Automated exploitation of sky polarization imagery.

We propose an automated method for detecting neutral points in the sunlit sky. Until now, detecting these singularities has been done manually. Results are presented that document the application of this method on a limited number of polarimetric images of the sky captured with a camera and rotating polarizer. The results are significant because a method for automatically detecting the neutral points may aid in the determination of the solar position when the sun is obscured and may have applications in meteorology and pollution detection and characterization.

Rectified Feature Matching for Spherical Panoramic Images

Spherical panoramic image processing has received renewed interest in the fields of photogrammetry and computer vision. The difficulty in spherical matching is largely due to inevitable image distortions introduced from the equirectangular projection. In this paper, we present an effective strategy for tackling the problem of distortion to improve the performance of spherical image matching. The effectiveness of the rectified matching is evaluated with simulated data and compared with state-of-art methods. In addition, experiments with respect to matching between omnidirectional and planar images, establishing 2D-to-3D correspondence with lidar, and the pose estimation of a spherical image sequence are conducted. The results verify the utility of the proposed method, which provides stable and evenly distributed corresponding points, and it is suitable for integration with conventional techniques for further 3D exploitation of imagery.

Spectral Radiance Modeling and Bayesian Model Averaging for Longwave Infrared Hyperspectral Imagery and Subpixel Target Identification

Hyperspectral imagery (HSI) exploitation typically requires spectral signatures for target detection and identification algorithms. As the longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, spectral radiance measurements are influenced by object temperature, and thus, estimates of target temperature may be necessary for emissivity retrieval to support these algorithms. Therefore, lack of accurate temperature information poses a significant challenge for HSI target detection and identification. Previous studies have demonstrated LWIR hyperspectral unmixing in both radiance and emissivity domains using in-scene target signatures. Here, a radiance-domain LWIR material identification algorithm for subpixel target identification of solid materials is developed by combining spectral radiance and linear mixing models with Bayesian model averaging. Application to experimental LWIR HSI illustrates that the algorithm effectively distinguishes between solid materials with a high degree of spectral similarity and reduces the probability of false alarms by at least one order of magnitude over a standard adaptive coherence estimator detector. Limits of identification are inferred from the imagery and found to depend on material type, target size, and target geometry. For the sensor and materials in this paper, the results imply that targets of nominally 5 m2 in size with strong spectral features can be identified for ground sampling distances (GSDs) on the order of 5–10 m (with abundances as low as ~10%) whereas blackbody-like materials are difficult to distinguish for GSDs larger than approximately 3 m.

Evaluation of optical remote sensing-based shallow water bathymetry for recursive mapping

Water depth estimation using optical remote sensing offers a reliable and efficient means of mapping coastal zones. Here, we aim to find a suitable model for fast and practical bathymetry of an estuary using Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS-3) images. The study examines three different models; (1) least square regression model, (2) spectral band-ratio method and (3) multi-tidal bathymetry model. The findings are supported with in situ observed depth values and statistical estimates. Although the least square regression model has provided best results with root mean square error (RMSE) of 0.4 m, it requires a large number of observed data points for absolute depth estimation. Spectral band-ratio and multi-tidal model provides results with RMSEs 2.1 and 0.9 m, respectively. The present investigation demonstrates that multi-date imagery exploitation at disparate tide levels is the best estimation technique for recursive shallow water bathymetry where in situ observation is not possible.

HYBRID-BASED DENSE STEREO MATCHING

Abstract. Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

HYBRID-BASED DENSE STEREO MATCHING

Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

Evaluating the Operational Retrieval and Downscaling of Urban Land Surface Temperatures

The Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing of the National Observatory (NOA/IAASARS) has developed a system that retrieves and downscales thermal remote sensing data obtained from Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) in real time. This activity aims to facilitate the operational monitoring of the urban thermal environment by providing sharpened data in a timely manner. The system entered its operational phase in April 2014 by producing quarter-hourly land surface temperature (LST) data at an enhanced spatial resolution (1 km) for five major European cities. The current work briefly presents the system and exhaustively validates the generated time-series for the three summer months of 2014. The performance assessment was carried out by comparing the entire archive of generated LST data (i.e., more than 30 000 images) with similar independent and well-validated products. The results obtained indicate that both the LST retrieval and LST downscaling modules, which form the heart of the system, perform satisfactorily. The wealth of information made available through such exploitation of MSG-SEVIRI imagery in real time has the potential to fill a large gap in monitoring the thermal urban environment and support effectively the timely generation of higher value products and services related to energy demand and heat-related health issue.

Reconstruction of individual trees from multi-aspect TomoSAR data

The localization and reconstruction of individual trees as well as the extraction of their geometrical parameters is an important field of research in both forestry and remote sensing. While the current state-of-the-art mostly focuses on the exploitation of optical imagery and airborne LiDAR data, modern SAR sensors have not yet met the interest of the research community in that regard. This paper presents a prototypical processing chain for the reconstruction of individual deciduous trees: First, single-pass multi-baseline InSAR data acquired from multiple aspect angles are used for the generation of a layover- and shadow-free 3D point cloud by tomographic SAR processing. The resulting point cloud is then segmented by unsupervised mean shift clustering, before ellipsoid models are fitted to the points of each cluster. From these 3D ellipsoids the relevant geometrical tree parameters are extracted. Evaluation with respect to a manually derived reference dataset prove that almost 74% of all trees are successfully segmented and reconstructed, thus providing a promising perspective for further research toward individual tree recognition from SAR data.

SEGMENTATION AND CROWN PARAMETER EXTRACTION OF INDIVIDUAL TREES IN AN AIRBORNE TOMOSAR POINT CLOUD

Abstract. The analysis of individual trees is an important field of research in the forest remote sensing community. While the current state-of-theart mostly focuses on the exploitation of optical imagery and airborne LiDAR data, modern SAR sensors have not yet met the interest of the research community in that regard. This paper describes how several critical parameters of individual deciduous trees can be extraced from airborne multi-aspect TomoSAR point clouds: First, the point cloud is segmented by unsupervised mean shift clustering. Then ellipsoid models are fitted to the points of each cluster. Finally, from these 3D ellipsoids the geometrical tree parameters location, height and crown radius are extracted. Evaluation with respect to a manually derived reference dataset prove that almost 86% of all trees are localized, thus providing a promising perspective for further research towards individual tree recognition from SAR data.

Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas

Shadows commonly exist in high resolution satellite imagery, particularly in urban areas, which is a combined effect of low sun elevation, off-nadir viewing angle, and high-rise buildings. The presence of shadows can negatively affect image processing, including land cover classification, mapping, and object recognition due to the reduction or even total loss of spectral information in shadows. The compensation of spectral information in shadows is thus one of the most important preprocessing steps for the interpretation and exploitation of high resolution satellite imagery in urban areas. In this study, we propose a new approach for global shadow compensation through the utilization of fully constrained linear spectral unmixing. The basic assumption of the proposed method is that the construction of the spectral scatter plot in shadows is analogues to that in non-shadow areas within a two-dimension spectral mixing space. In order to ensure the continuity of land covers, a smooth operator is further used to refine the restored shadow pixels on the edge of non-shadow and shadow areas. The proposed method is validated using the WorldView-2 multispectral imagery collected from downtown Toronto, Ontario, Canada. In comparison with the existing linear-correlation correction method, the proposed method produced the compensated shadows with higher quality.