Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness

Hodgson, Michael E.; Jensen, John R.; Tullis, Jason A.; Riordan, K.; Archer, Clark M.

doi:10.14358/pers.69.9.973

Cited by 148 publications

(80 citation statements)

References 16 publications

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“…Additionally, LiDAR benefits from a low processing requirement and high spatial resolution [6,7]. LiDAR data has proven competent in improving up to 25% accuracy in differentiating surfaces by means of height [4]. This study area contains asphalt roads and asphalt roofs which possess similar spectral reflectance; therefore, it is necessary to make a distinction between roof and ground surfaces.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Nimbalkar

Jarocińska

Zagajewski

2018

Journal of Spectroscopy

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Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth's object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.

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Section: Methodsmentioning

confidence: 99%

“…The distribution of varied urban surface materials has an impact on the surrounding environment [4,5]. This has requested the detailed accounting of surface materials and its properties, such as magnitude, abundance, location, geometry, and spatial pattern [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Nimbalkar

Jarocińska

Zagajewski

2018

Journal of Spectroscopy

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“…It is thus easy to adapt for new datasets, and the classification process is highly automated. In the context of ALS and aerial image data analysis, classification trees have been used, for example, by Hodgson et al [65] for mapping of urban parcel imperviousness, Ducic et al [66] to classify full-waveform laser data, and Jung [13], Matikainen [67], Zingaretti et al [68], Holland et al [8] and Im et al [69] to classify buildings and other classes. The classification tree tools available in the Statistics Toolbox of Matlab were used to implement the classification.…”

Section: Building Detection Methodsmentioning

confidence: 99%

Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

et al. 2010

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Abstract:There is currently high interest in developing automated methods to assist the updating of map databases. This study presents methods for automatic detection of buildings and changes in buildings from airborne laser scanner and digital aerial image data and shows the potential usefulness of the methods with thorough experiments in a 5 km 2 suburban study area. 96% of buildings larger than 60 m 2 were correctly detected in the building detection. The completeness and correctness of the change detection for buildings larger than 60 m 2 were about 85% (including five classes). Most of the errors occurred in small or otherwise problematic buildings.

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“…For an object in the M1, 1) if it is totally in the interior of the changed areas, its class can only be determined according to the independent classification result from the high resolution image; 2) if this object is intersected with the changed areas, it means that the class type of this object should be consistent with the class type of the intersection areas between this object and unchanged areas. Pervious research show that joint classification with stacked LiDAR data and high resolution image generally has a better performance than the independent classification result yielded by sole sensor data [37]. Thus, the class type of this object is determined by the class extracted from joint classification result located in the intersection between unchanged areas and this object.…”

Section: Object-based Post-classification Fusionmentioning

confidence: 99%

“…Generally, LiDAR data is firstly transformed into a multi-band raster image, and then used as integrated features with high resolution multispectral image for landscape classification [34][35][36]. Previous studies indicated that the combination of LiDAR data and high resolution remote sensing image outperformed that using only high resolution image [24,37,38].…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Impervious Surface Mapping Incorporating Lidar Data and High-Resolution Imagery at Different Acquisition Times

Luo¹,

Wang²,

Wu³

et al. 2018

Preprint

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Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multisensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and highresolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an objectbased post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

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Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness

Abstract: Abstract

Cited by 148 publications

References 16 publications

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

An Improved Method for Impervious Surface Mapping Incorporating Lidar Data and High-Resolution Imagery at Different Acquisition Times

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