A free set of tools for automated imagery rectification

2006

Remote sensing efforts are being employed as part of the recently initiated “Environmentally Conscious Precision Agriculture” project at University of Maryland Eastern Shore. Autonomous and remote controlled aerial platforms with nadir view cameras are being utilized in conjunction with precision farming equipment. Initial efforts have involved development of project specific tools utilizing commercial software environments for rectification, georeferencing, and mosaicking of image frames acquired using small remotely operated airborne platforms. This paper will delineate image acquisition, rectification, georeferencing, and mosaicking of image frames to generate a composite image of the entire field. Yield data collected by a yield monitoring system is compared with information obtained from the images.

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014

Modern satellites tag their images with geo-location information using GPS and star tracking systems. Depending on the quality of the geo-positioning equipment, geo-location errors may range from a few meters to tens of meters on the ground. At the current state of art, there is not an established method to automatically correct these errors limiting the large-scale utilization of the satellite imagery. In this paper, an automatic geolocation correction framework that corrects multiple satellite images simultaneously is presented. As a result of the proposed correction process, all the images are effectively registered to the same absolute geodetic coordinate frame. The usability and the quality of the correction framework are shown through probabilistic 3-D surface model reconstruction. The models given by original satellite geo-positioning meta-data and the corrected meta-data are compared and the quality difference is measured through an entropy-based metric applied onto the high resolution height maps given by the 3-D models. Measuring the absolute accuracy of the framework is harder due to lack of publicly available high precision ground surveys; however, the geo-location of images of exemplar satellites from different parts of the globe are corrected and the road networks given by OpenStreetMap are projected onto the images using original and corrected meta-data to show the improved quality of alignment.

To date, aerial archaeologists generally apply simple rectification procedures or more expensive and time-consuming orthorectification algorithms to correct their aerial photographs in varying degrees for geometrical deformations induced by the topographical relief, the tilt of the camera axis and the distortion of the optics. Irrespective of the method applied, the georeferencing of the images is commonly determined with ground control points, whose measurement and identification is a time-consuming operation and often limits certain images from being accurately georeferenced. Moreover, specialised software, certain photogrammetric skills, and experience are required. Thanks to the recent advances in the fields of computer vision and photogrammetry as well as the improvements in processing power, it is currently possible to generate orthophotos of large, almost randomly collected aerial photographs in a straightforward and nearly automatic way. This paper presents a computer vision-based approach that is complemented by proven photogrammetric principles to generate orthophotos from a range of uncalibrated oblique and vertical aerial frame images. In a first phase, the method uses algorithms that automatically compute the viewpoint of each photograph as well as a sparse 3D geometric representation of the scene that is imaged. Afterwards, dense reconstruction algorithms are applied to yield a three-dimensional surface model. After georeferencing this model, it can be used to create any kind of orthophoto out of the initial aerial views. To prove the benefits of this approach in comparison to the most common ways of georeferencing aerial imagery, several archaeological case studies are presented. Not only will they showcase the easy workflow and accuracy of the results, but they will also prove that this approach moves beyond current restrictions due to its applicability to datasets that were previously thought to be unsuited for convenient georeferencing.

2006

Geo-referencing through rectification remains to be one of the challenging problems in remote sensing and various imagery applications such as image pose estimation, image to Object Registration and 3D map generation. Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the image space and object space. With government funded satellites, access to calibration and ephemeris data allowed the development of these models. However, for commercial high- resolution satellites, these data have been withheld from users, and therefore alternative empirical rectification models have been developed. In general, most of these models are based on the use of control points. Visibility and uniqueness of distinct control points in the input imagery limit use of this feature for robust Georeferencing procedure and provide a catalyst for the development of algorithms based on other image features. Recent advances in digital photogrammetry and Remot...

ISPRS International Journal of Geo-Information

As one of the earliest forms of remote sensing, aerial photography has been regarded as an important part of the mapmaking process. Aerial photos, especially historical aerial photos, provide significant amount of valuable information for many applications and fields. However, due to limited funding support, most historical aerial photos have not been digitized and georeferenced yet, which substantially limits their utility for today’s computer-based image processing and analysis. Traditionally, historical aerial photos are georeferenced with desktop GIS software applications. However, this method is expensive, time-consuming, and labor-intensive. To address these limitations, this research developed a custom-built online georeferencing tool to enable georeferencing digitized historical aerial photos in a web environment, which is able to georeference historical aerial photos in a rapid and cost-effective manner. To evaluate the georeferencing performance, a set of 50 historical aer...

2015

This paper implements and evaluates experimentally a procedure for automatic georeferencing of images acquired by UAV’s (Unmanned Aerial Vehicles) for agriculture. The body attitude and position with respect to the navigation frame are supposed to be available, which is a reasonable hypothesis, since they are needed anyway for implementing the UAV autopilot and the guidance system. Since the camera model is necessary for georeferencing, this paper also proposes a completely automatic procedurefor camera calibration, i.e., estimation of camera and lens distortion parameters. Flight data are employed for evaluating the georeferencing performance.

ISPRS Congress, 2004

For various satellite imagery applications, geo-referencing through rectification is a common operation. Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the image space and object space. With government funded satellites, access to calibration and ephemeris data allowed the development of these models. However, for commercial high-resolution satellites, these data have been withheld from users, and therefore alternative empirical rectification models have been developed. In general, most of these models are based on the use of control points. The lack of control points in some remote areas such as deserts, forests and mountainous areas provides a catalyst for the development of algorithms based on other image features. One of the alternatives is to use linear features obtained from scanning/digitizing hardcopy maps, from terrestrial mobile mapping systems or from digital images. In this work, a new model named the Line Based Transformation Model (LBTM) is established for satellite imagery rectification. The model has the flexibility to either solely use linear features or use linear features and a number of control points to define the image transformation parameters. As with other empirical models, the LBTM does not require any sensor calibration or satellite ephemeris data. The underlying principle of the model is that the relationship between line segments of straight lines in the image space and the object space can be expressed by affine or conformal relationships. Synthetic as well as real data have been used to check the validity and fidelity of the model, and the results show that the LBTM performs to a level comparable with existing point based transformation models.

Precision Agriculture, 2019

The global spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs) is relatively low. The use of ground control points (GCPs) improves the accuracy but it requires RTK-GNSS (global navigation satellite system) measurements in the field. The objective of this study was to evaluate manual geo-rectification after ortho-mosaicking as a cost-effective alternative to GCPs or UAVs equipped with RTK-GNSS. Google Earth images and A-B lines used for tractor auto-steering were used for manual geo-rectification with a free geographic information system (GIS) and remote-sensing (RS) software (QGIS). Two different photogrammetry software (Agisoft Photoscan and Pix4DMapper) were used for ortho-mosaicking. Images were captured in three fields at two altitudes (40 and 80 m) with a multi-rotor camera drone equipped with a GNSS without ground reference (Phantom 4). The results showed that flight altitude and photogrammetry software had no impact on the spatial accuracy and that manual geo-rectification significantly improved the spatial accuracy. Root mean squares (RMSEs) for orthomosaics without geo-rectification was in the range of 4 to 28 m and the range was 0.6 to 2.5 m and 0.5 to 1.1 m for geo-rectification based on Google Earth images and A-B-lines, respectively. RTK-GNSS tagged UAV images and the use of GCPs gave average RMSEs of less than 0.1 m. It was concluded that manual geo-rectification offers a feasible alternative to GCPs and UAVs with RTK-GNSS when spatial accuracy of about 1 m is acceptable.

Defence Science Journal, 2011

A number of algorithms have been reported to process and remove geometric distortions in satellite images. Ortho-correction, geometric error correction, radiometric error removal, etc are a few important examples. These algorithm require supplementary meta-information of the satellite images such as ground control points and correspondence, sensor orientation details, elevation profile of the terrain, etc to establish corresponding transformations. In this paper, a pre-processing algorithm has been proposed which removes systematic distortions of a satellite image and thereby removes the blank portion of the image. It is an input-to-output mapping of image pixels, where the transformation computes the coordinate of each output pixel corresponding to the input pixel of an image. The transformation is established by the exact amount of scaling, rotation and translation needed for each pixel in the input image so that the distortion induced during the recording stage is corrected.