Aparajithan Sampath

Building boundary tracing and regularization from airborne lidar point clouds

Building boundary is necessary for the real estate industry, flood management, and homeland security applications. The extraction of building boundary is also a crucial and difficult step towards generating city models. This study presents an approach to the tracing and regularization of building boundary from raw lidar point clouds. The process consists of a sequence of four steps: separate building and non-building lidar points; segment lidar points that belong to the same building; trace building boundary points; and regularize the boundary. For separation, a slope based 1D bi-directional filter is used. The segmentation step is a region-growing approach. By modifying a convex hull formation algorithm, the building boundary points are traced and connected to form an approximate boundary. In the final step, all boundary points are included in a hierarchical least squares solution with perpendicularity constraints to determine a regularized rectilinear boundary. Our tests conclude that the uncertainty of regularized building boundary tends to be linearly proportional to the lidar point spacing. It is shown that the regularization precision is at 18 percent to 21 percent of the lidar point spacing, and the maximum offset of the determined building boundary from the original lidar points is about the same as the lidar point spacing. Limitation of lidar data resolution and errors in previous filtering processes may cause artefacts in the final regularized building boundary. This paper presents the mathematical and algorithmic formulations along with stepwise illustrations. Results from Baltimore city, Toronto city, and Purdue University campus are evaluated.

Urban DEM Generation from Raw Lidar Data: A Labeling Algorithm and its Performance

This paper addresses the separation of ground points from raw lidar data for bald ground digital elevation model (DEM) generation in urban areas. This task is considered to be a labeling process through which a lidar point is labeled as either a ground point or a non-ground point. Mathematical formulation is presented to define the ground. A new approach is proposed that conducts one-dimensional labeling in two opposite directions followed by a linear regression, both along the lidar scan line profile. The study shows that the one-dimensional characteristic makes the calculation efficient, and the reliability is assured by the bidirectional labeling process. Lidar data over suburban and downtown Baltimore (Maryland), Osaka (Japan), and Toronto (Canada) are used for the study. Quality assessment is designed and conducted to investigate the performance of the labeling approach by using manually-selected ground truth. It is shown that 2.7 percent ground points are wrongly labeled as building points, and 2.6 percent building points are mistakenly labeled as ground points over the four study areas. Detailed graphic and numerical results are provided to illustrate the proposed labeling approach and its performance for complex urban areas.

Radiometric and geometric assessment of data from the RapidEye constellation of satellites

To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earths surface using imagery acquired from multiple spaceborne imaging sensors. The RapidEye (RE) satellite constellation acquires high-resolution satellite images covering the entire globe within a very short period of time by sensors identical in construction and cross-calibrated to each other. To evaluate the RE high-resolution Multi-spectral Imager (MSI) sensor capabilities, a cross-comparison between the RE constellation of sensors was performed first using image statistics based on large common areas observed over pseudo-invariant calibration sites (PICS) by the sensors and, second, by comparing the on-orbit radiometric calibration temporal trending over a large number of calibration sites. For any spectral band, the individual responses measured by the five satellites of the RE constellation were found to differ <2–3% from the average constellation response depending on the method used for evaluation. Geometric assessment was also performed to study the positional accuracy and relative band-to-band (B2B) alignment of the image data sets. The position accuracy was assessed by comparing the RE imagery against high-resolution aerial imagery, while the B2B characterization was performed by registering each band against every other band to ensure that the proper band alignment is provided for an image product. The B2B results indicate that the internal alignments of these five RE bands are in agreement, with bands typically registered to within 0.25 pixels of each other or better.

Validation of Geometric Accuracy of Global Land Survey (GLS) 2000 Data

Abstract The Global Land Survey (GLS) 2000 data were generated from Geocover™2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, ortho-rectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.

Modern Technologies for Design Data Collection

Design data collection involving the use of Lidar instrument, in conjunction with Global Positioning System (GPS) proves to be very effective. Data required to model two bridges over the I-70 were collected on a single day, involving five and six sessions with Lidar equipment. Even though the data were collected on two bridges, it did not cause any disruption of the traffic, either on the Interstate or on the bridges. A major cause of concern during survey activities, particularly along interstates is safety, both for the motorists as well as the people involved in data collection. Lidar data collection was found to be extremely safe in both aspects. The whole process of collecting Lidar data and GPS coordinates for control was completed in 2 days for both bridges. Office work involved combining the GPS data with conventional survey data to bring control on six pre-selected points within the Lidar point cloud. This control information was later used to bring the point cloud into a geographic coordinate system. This survey provided the means to compare the 3D point cloud with bridge designs that were created using other methods of data collection. It was found that the 3D point cloud exhibits a very high degree of accuracy, both internally and also when georeferenced independently using GPS and conventional control survey. The Lidar model was compared to the MXRoad data model provided by Indiana Department of Transportation (INDOT). The discrepancies between the two models were not larger than 0.125 ft/3.81 cm horizontally and 0.05 ft/1.52 cm vertically. The data collected completely modeled the bridge and the accuracy of the data ensures that any model of the bridge, either as a whole or in part, will correctly reflect the current state of the bridge. The data collected can also be used for various applications including cut-and-fill estimates, modeling the state of the bridge, making measurements on various parts of the bridge. A cause of concern is the amount of data involved. As millions of 3D points are collected, popular Computer Aided Design/Geographic Information System (CAD)/(GIS) packages are unable to deal with it. For this reason proprietary software, designed particularly to handle such huge volumes of data involved, was used for analyzing this data. However, it is possible to export data from this software to other commonly used CAD packages. Using satellite imagery instead of aerial photos may provide faster results to investigate the project area. Conversion of the MXROAD data into the ArcGIS system is not easy, but it is hoped that this problem can be solved very easily. The Lidar point cloud should be processed and a CAD model of the data should be obtained to obtain more useful information. With the help of the GIS a variety of data sources and types can be integrated, visualized and used to make about resource management, and perform modeling and analysis. GIS helps organize bridge management information contained in various forms, such as inspection reports, rehab plans, and CAD files. Maintenance management and asset valuation may be enhanced with GIS and linear referencing systems.

Monitoring the long term stability of the IRS-P6 AWiFS sensor using the Sonoran and RVPN sites

This paper focuses on radiometric and geometric assessment of the Indian Remote Sensing (IRS-P6) Advanced Wide Field Sensor (AWiFS) sensor using the Sonoran desert and Railroad Valley Playa, Nevada (RVPN) ground sites. Imageto- Image (I2I) accuracy and relative band-to-band (B2B) accuracy were measured. I2I accuracy of the AWiFS imagery was assessed by measuring the imagery against Landsat Global Land Survey (GLS) 2000. The AWiFS images were typically registered to within one pixel to the GLS 2000 mosaic images. The B2B process used the same concepts as the I2I, except instead of a reference image and a search image; the individual bands of a multispectral image are tested against each other. The B2B results showed that all the AWiFS multispectral bands are registered to sub-pixel accuracy. Using the limited amount of scenes available over these ground sites, the reflective bands of AWiFS sensor indicate a long-term drift in the top-of-atmosphere (TOA) reflectance. Because of the limited availability of AWiFS scenes over these ground sites, a comprehensive evaluation of the radiometric stability using these sites is not possible. In order to overcome this limitation, a cross-comparison between AWiFS and Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) was performed using image statistics based on large common areas observed by the sensors within 30 minutes. Regression curves and coefficients of determination for the TOA trends from these sensors were generated to quantify the uncertainty in these relationships and to provide an assessment of the calibration differences between these sensors.