Airborne hybrid sensor maps the country: Multi-agency effort for testing a potential new hybrid 3DEP-NAIP sensor
No abstract available.
Colorado↗
Geology topics
Aparajithan Sampath
Publications and source records attributed to Aparajithan Sampath.
Positional accuracy assessment of lidar point cloud from NAIP/3DEP pilot project
The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy the U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation Program (3DEP) and the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor has the potential to meet current and future 3DEP topographic lidar collection requirements. We performed an accuracy analysis and assessment on the lidar point cloud produced from CountryMapper. The boresighting calibration and co-registration by georeferencing correction based on ground control points are assumed to be performed by the data provider. The scope of the accuracy assessment is to apply the following variety of ways to measure the accuracy of the delivered point cloud to obtain the error statistics. Intraswath uncertainty from a flat surface was computed to evaluate the point cloud precision. Intraswath difference between opposite scan directions and the interswath overlap difference were evaluated to find boresighting or any systematic errors. Absolute vertical accuracy over vegetated and non-vegetated areas were also assessed. Both horizontal and vertical absolute errors were assessed using the 3D absolute error analysis methodology of comparing conjugate points derived from geometric features. A three-plane feature makes a single unique intersection point. Intersection points were computed from ground-based lidar and airborne lidar point clouds for comparison. The difference between two intersection points form one error vector. The geometric feature-based error analysis was applied to intraswath, interswath, and absolute error analysis. The CountryMapper pilot data appear to satisfy the accuracy requirements suggested by the USGS lidar specification, based upon the error analysis results. The focus of this research was to demonstrate various conventional accuracy measures and novel 3D accuracy techniques using two different error computation methods on the CountryMapper airborne lidar point cloud.
Remote Sensing↗
Geometric quality assessment of lidar data based on swath overlap
This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the inter-swath errors, including the presence of systematic errors in lidar data. Hence they pose a risk to the user in terms of data acceptance (i.e. a higher potential for Type II error indicating risk of accepting potentially unsuitable data). For example, if the overlap area is too small or if the sampled locations are close to the center of overlap, or if the errors are sampled in flat regions when there are residual pitch errors in the data, the resultant Root Mean Square Differences (RMSD) can still be small. To avoid this, the following are suggested to be used as criteria for defining the inter-swath quality of data: a) Median Discrepancy Angle b) Mean and RMSD of Horizontal Errors using DQM measured on sloping surfaces c) RMSD for sampled locations from flat areas (defined as areas with less than 5 degrees of slope) It is suggested that 4000-5000 points are uniformly sampled in the overlapping regions of the point cloud, and depending on the surface roughness, to measure the discrepancy between swaths. Care must be taken to sample only areas of single return points only. Point-to-Plane distance based data quality measures are determined for each sample point. These measurements are used to determine the above mentioned parameters. This paper details the measurements and analysis of measurements required to determine these metrics, i.e. Discrepancy Angle, Mean and RMSD of errors in flat regions and horizontal errors obtained using measurements extracted from sloping regions (slope greater than 10 degrees). The research is a result of an ad-hoc joint working group of the US Geological Survey and the American Society for Photogrammetry and Remote Sensing (ASPRS) Airborne Lidar Committee.
Conference Paper↗
Validation of geometric accuracy of Global Land Survey (GLS) 2000 data
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, orthorectified 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.
Photogrammetric Engineering and Remote Sensing↗
ASPRS research on quantifying the geometric quality of lidar data
The ASPRS Lidar Cal/Val (calibration/validation) Working Group led by the US Geological Survey (USGS) to establish “Guidelines on Geometric Accuracy and Quality of Lidar Data” has made excellent progress via regular teleconferences and meetings. The group is focused on identifying data quality metrics and establishing a set of guidelines for quantifying the quality of lidar data. The working group has defined and agreed on lidar Data Quality Measures (DQMs) to be used for this purpose. The DQMs are envisaged as the first ever consistent way of checking lidar data. It is expected that these metrics will be used as standard methods for quantifying the geometric quality of lidar data. The goal of this article is to communicate these developments to the readers and the larger geospatial community and invite them to participate in the process.
Photogrammetric Engineering and Remote Sensing↗
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 Earth's 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.
International Journal of Remote Sensing↗
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.
Conference Paper↗
Geometric evaluation and validation of aerial and satellite data using Sioux Falls Geometric test range
No abstract available.
Conference Paper↗
Self calibration of small and medium format digital cameras
The knowledge of a camera’s interior orientation parameters are a prerequisite for the camera to be used in any precision photogrammetric project. Historically, the interior orientation parameters have been determined by analyzing the measured ground 3D coordinates of photo-identifiable targets, and their 2D (image) coordinates from multiple images of these targets. Camera self calibration, on the other hand, uses targets on a scene that have not been measured before. In this research, we will briefly discuss existing self calibration techniques, and present two methods for camera self calibration that are being used at the U.S. Geological Survey’s Earth Resources Observation and Science (EROS) Data Center. The first method, developed by Pictometry (augmented by Dr C.S. Fraser), uses a series of coded targets on a cage. The coded targets form different patterns that are imaged from nine different locations with differing camera orientations. A free network solution using collinearity equations is used to determine the calibration parameters. The coded targets are placed on the cage in three different planes, which allows for a robust calibration procedure. The USGS/EROS has developed an inexpensive method for calibration, particularly for calibrating short focal length cameras. In this case, the coded targets are pasted on a small prototype box and imaged from different locations and camera orientations. The design of the box is discussed, and the results of the box and the cage calibrations are compared and analyzed.
Conference Paper↗