Wai Yeung Yan

Radiometric Correction and Normalization of Airborne LiDAR Intensity Data for Improving Land-Cover Classification

Radiometric correction of airborne LiDAR intensity data has been proposed based on the use of the radar (range) equation for removing the effects of attenuation due to system and environmental-induced distortions. Although radiometric correction of airborne LiDAR intensity data has been recently investigated with results revealing improved accuracy of surface classification, there exist a few voids requiring further research effort. First, the variation of object surface characteristics (slope and aspect) plays a crucial role in modeling the recorded intensity data, and thus, the laser incidence angle is usually considered in the correction process. Nevertheless, the use of incidence angle would lead to the effects of overcorrection, particularly on those features located in steep slope. Second, line-stripping problems are usually appeared in the overlapping region of LiDAR data strips acquired by sensors configured with automatic gain control (AGC). Currently, the effects of AGC cannot be perfectly modeled due to the nondisclosure of information by the sensor manufacturers. In this paper, we attempt to fill these voids by: 1) proposing a correction mechanism using the surface slope as a threshold to select either using scan angle or incidence angle in the radar (range) equation; and 2) proposing a subhistogram matching technique to radiometrically normalize the overlapping intensity data. The proposed approaches were applied to three real airborne LiDAR data strips for experimental testing. The results showed that the coefficient of variation reached to the lowest value for most of the land-cover features with a slope threshold between 30° and 40°. The variance-to-mean ratio of five land-cover features was significantly reduced by 70%-82% after applying the proposed correction mechanism. In addition, the systematic noises appeared in the overlapping region were significantly reduced after radiometric correction and normalization, where the overall accuracies were improved by up to 16.5% in the results of intensity data classification. With the demonstrated improvement in intensity homogeneity, it is recommended that airborne LiDAR intensity data should be radiometrically preprocessed before performing any thematic applications.

Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products

LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data.

Using Stereo Satellite Imagery for Topographic and Transportation Applications: An Accuracy Assessment

The launch of the Very High Resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of satellite stereo imaging for many applications. The objective of this paper is to evaluate the level of accuracy that can be achieved by using stereo satellite images for different applications involving significantly different types of terrain. Three mathematical models for satellite sensor modeling are used: Rational Function Model (RFM), 3D polynomial model, and 3D affine model. Three stereo pairs of image datasets are tested from different satellites for different areas: (a) Indian Remote Sensing (IRS)-1D stereo images for topographic mapping and digital terrain elevation modeling for an area in Egypt; (b) IKONOS stereo images for highway alignments extraction in Toronto, Canada; and (c) IKONOS stereo images for topographic mapping and geometric parameter extraction for highway alignments in Hong Kong, China. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from conventional techniques, including Global Positioning System, field measurements, and aerial photogrammetry. The accuracy of the extracted features was found to be within a pixel-level. The results of this paper should be of interest to professionals from different disciplines exploring the use and accuracy of satellite stereo images for topographic and transportation applications.

Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites.

This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites’ land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring.

Construction of digital 3D highway model using stereo IKONOS satellite imagery

This study aims to assess the accuracy of using stereo high resolution satellite imagery for extracting the highway profiles and plans and constructing accurate 3D highway visualization model. Two stereo-pair IKONOS satellite images for Hong Kong and Toronto are geo-referenced by using a number of ground control points acquired by global positioning system measurements. A polynomial-based generic pushbroom model and rational function model are used to perform the sensor orientation, respectively. The highway alignments are extracted semi-automatically using stereoscopic measurements, and a 3D digital model along the highway is constructed. It is found that the highway alignments retrieved from the stereo IKONOS images result in less than 1-m root mean squared error in most of the cases in the horizontal and vertical directions. Near half-pixel accuracy can be achieved by using pansharpening stereo satellite imagery and under the condition that clear road surface markings can be identified along the highway.

Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise

ABSTRACT Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose. One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region. Though recent researches have proposed physical and empirical approaches for intensity data correction, the effect of striping noise has not yet been resolved. This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap. The normalization technique is built based on a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The proposed method was tested with two individual LiDAR datasets collected by Teledyne Optechs Gemini (1064 nm) and Orion (1550 nm) sensors. The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification. The coefficient of variation of five selected land cover features was reduced by 19–65%, where a 9–18% accuracy improvement was achieved in different classification scenarios. With the proven capability of the proposed method, both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.

Panchromatic IKONOS image classification using wavelet based features

This study investigates the use of wavelet decomposed features for panchromatic image classification for the purpose of urban land-use mapping. Discrete Wavelet Transform (DWT) is recently found to be a promising tool in image analysis of both spatial and frequency domain, as DWT has the ability to examine the signal at different resolutions and desired scales. Although DWT has been applied in different image analysis applications such as image fusion, image compression and edge detection, it is less applied in image classification technique. In this study, a Very High Resolution (VHR) IKONOS satellite image in panchromatic (PAN) mode (1-m spatial resolution) is used to examine and assess the use of the DWT for image classification of urban areas. Experimental work are conducted by comparing the image classification accuracy of the original PAN IKONOS image and the wavelet decomposed images using Haar wavelet by applying two parametric classifiers: Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). A preliminary investigation has been carried out to assess the effect of wavelet decomposition level towards the classification accuracy. It is found that 5% and 3% improvement of the accuracy are recorded by applying the first level wavelet decomposition using LDA and QDA, respectively. Improvement of 7% and 6% by applying second level wavelet decomposition is also found using LDA and QDA, respectively. Although the overall accuracy is only near 40%, DWT is demonstrated as a viable and promising method to improve the PAN image classification accuracy, especially when it is introduced to determine those heterogeneous land classes in urban areas.

Polygon-based image registration: a new approach for geo-referencing historical maps

ABSTRACT This paper presents a new approach for geo-referencing historical maps using a polygon-based image registration technique. Since most historical maps lack long lasting point features to serve as control primitives for image registration, we explore the use of polygon features as control primitives that can be identified in both the map and the geo-referenced coordinate system based on matching the polygons’ shape context. A coordinate transformation model can be established using the matched vertices of the polygons, and the model coefficients are subsequently estimated using least-squares adjustment. The proposed method was tested on a digitized lithographic map of downtown Toronto, Ontario, Canada, created in 1857. The experimental work showed a good agreement for the image registration where the Dice similarity coefficient was 0.8 (i.e. 80% of overlap found between the two sets of polygons), regardless of using affine or polynomial model.

Potential Accuracy of Traffic Signs' Positions Extracted From Google Street View

This work demonstrates the potential use of Google Street View (GSV) in engineering measurements. An investigation was conducted to assess the geopositioning accuracy of traffic signs extracted from GSV. A direct linear transformation (DLT) model is used to establish the relationship between the GSV image coordinate system and the ground coordinate system with the aid of ground control points (GCPs). The ground coordinates of the traffic sign can be retrieved by using the solved DLT coefficients. It is found that the root-mean-square (RMS) error of the extracted traffic signs location is less than 1 m in general. By increasing the number of GSV images and GCPs, the RMS error can be further reduced to 0.5 m or less. This preliminary study demonstrates a viable solution to extract the location of traffic signs from GSV.

The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.