The application of quantitative performance evaluation methods can provide useful insights in determining the utility of computer-assisted methods for delineating geographic features from remotely sensed images. Evaluation concepts are demonstrated with road centerlines in particular, but are applicable to similar feature types such as paths, trails, or rivers. The two comparative measures used to differentiate conventional versus computer-assisted delineation are 1) user clock time, and 2) spatial consistency. Our evaluation results with road centerlines demonstrate how such quantitative analyses can be used to determine the utility of computer-assisted methods from both developmental and operational perspectives.

From an operational standpoint, road extraction remains largely a manual process despite the existence of several commercially available automation tools. The problem of automated feature extraction (AFE) in general is a challenging task as it involves the recognition, delineation, and attribution of image features. The efficacy of AFE algorithms in operational settings is difficult to measure due to the inherent subjectivity involved. Ultimately, the most meaningful measures of an automation method are its effect on productivity and actual utility. Several quantitative and qualitative factors go into these measures including spatial accuracy and timed comparisons of extraction, different user training levels, and human-computer interface issues. In this paper we investigate methodologies for evaluating automated road extraction in different operational modes. Interactive and batch extraction modes of automation are considered. The specific algorithms investigated are the GeoEye Interactive Road Tracker®(IRT) and the GeoEye Automated Road Tracker®(ART) respectively. Both are commercially available from GeoEye. Analysis metrics collected are derived from timed comparisons and spatial delineation accuracy. Spatial delineation accuracy is measured by comparing algorithm output against a manually derived image reference. The effect of object-level fusion of multiple imaging modalities is also considered. The goal is to gain insight into measuring an automation algorithm's utility on feature extraction productivity. Findings show sufficient evidence to demonstrate a potential gain in productivity when using an automation method when the situation is warranted. Fusion of feature layers from multiple images also demonstrates a potential for increased productivity compared to single or pair-wise combinations of feature layers.

The adoption of rational functions as a preferred sensor orientation model for narrow field of view line scanner imagery accompanied the introduction of commercial high-resolution satellite imagery (HRSI) at the turn of the millennium. This paper reviews the developments in ground point determination from HRSI via the model of terrain independent rational polynomial coefficients (RPCs). A brief mathematical background to rational functions is first presented, along with a review of the models for generating RPCs from a rigorous sensor orientation, and for geopositioning via either forward intersection or monoplotting. The concept of RPC block adjustment with compensation for exterior orientation biases is then discussed, as is the means to enhance the original RPCs through a bias correction procedure. The potential for RPC block adjustment to yield sub-pixel geopositioning accuracy from HRSI is illustrated using results from experimental testing with two Quickbird stereo image pairs and three multi-image IKONOS blocks. Finally, error propagation issues in RPC block adjustment of HRSI are considered.

Block adjustment of high-resolution pushbroom satellite images, such as those collected by Ikonos, differs significantly from classical aerial triangulation. Error propagation in block adjustment of such images, and hence the final mapping accuracy, depends on multiple factors such as image collection geometry, image collection mode (mono or stereo), and distribution of ground control. This paper discusses the influence of these factors and presents a simplified method for accuracy pre-analysis of block adjustment of high-resolution satellite images. The proposed accuracy pre-analysis methodology is demonstrated using simulated monoscopic and stereo Ikonos image blocks, and the introduction of a cross strip is presented to demonstrate its effect on improving the accuracy of monoscopic blocks. The predictions of the pre-analysis model are validated using two real-world Ikonos mapping blocks.

This paper describes how to block adjust high-resolution satellite imagery described by Rational Polynomial Coefficient (RPC) camera models and illustrates the method with an Ikonos example. By incorporating a priori constraints into the adjustment model, multiple independent images can be adjusted with or without ground control. The RPC block adjustment model presented in this paper is directly related to geometric properties of the physical camera model. Multiple physical camera model parameters having the same net effect on the object-image relationship are replaced by a single adjustment parameter. Consequently, the proposed method is numerically more stable than the traditional adjustment of exterior and interior orientation parameters. This method is generally applicable to any photogrammetric camera with a narrow field of view, calibrated, stable interior orientation and accurate a priori exterior orientation data. As demonstrated in the paper, for Ikonos satellite imagery, the RPC block adjustment achieves the same accuracy as the ground station block adjustment with the full physical camera model.

The IKONOS satellite simultaneously collects 1-m panchromatic and 4-m multispectral images, providing the commercial and scientific community with a dramatic improvement in spatial resolution over previously available satellite imagery. The sun-synchronous IKONOS orbit provides global coverage, consistent access times, and near-nadir viewing angles. The system is capable of 1:10,000 scale mapping without ground control and 1:2400 scale mapping with ground control. The IKONOS ground station produces radiometrically corrected images, georectified images, orthorectified images, stereo pairs, and digital elevation models (DEMs) for image analysis, photogrammetric, and cartographic applications. This article provides an overview of the IKONOS satellite, ground systems, products, and applications.

The IKONOS satellite simultaneously collects 1-m panchromatic and 4-m multispectral images, providing the commercial and scientific community with a dramatic improvement in spatial resolution over previously available satellite imagery. The sun-synchronous IKONOS orbit provides global coverage, consistent access times, and near-nadir viewing angles. The system is capable of 1:10,000 scale mapping without ground control and 1:2400 scale mapping with ground control. The IKONOS ground station produces radiometrically corrected images, georectified images, orthorectified images, stereo pairs, and digital elevation models (DEMs) for image analysis, photogrammetric, and cartographic applications. This article provides an overview of the IKONOS satellite, ground systems, products, and applications. D 2003 Elsevier Inc. All rights reserved.

The ground-to-image relationship of an IKONOS image is described by its nominal RPC camera geometry supplemented with bias and drift parameters. Experimental data shows that the RMS bias is 4-meters and the RMS drift is 50 PPM. Residual errors after bias and drift correction are 0.5 meters RMS. A mathematical model to estimate ground coordinates from block-adjusted imagery is developed. Experimental results for this point measurement process will be presented at the conference.

ABSTRACT: Since its launch in September of 1999, the IKONOS satellite has been consistently providing high quality 1-meter panchromatic and 4-meter multispectral images. Accurate interior and exterior orientation enable IKONOS to achieve high geometric accuracy with or without ground control. Exterior orientation is determined by on-board GPS receivers, star trackers, gyros, and interlock angles. Post-processing of GPS data with software incorporating sophisticated filtering and orbital modeling algorithms results in accurate ephemeris. Kalman,filtering of gyro and star tracker data results in optimal combination,of lower frequency star tracker attitude data exhibiting high absolute accuracy with high frequency gyro data being very accurate over short time intervals. Interlock angles relate the attitude and the camera,coordinate systems and have been calibrated both pre-launch and in-flight. Initial interior orientation parameter values were determined by pre-launch measurements and later refined by in-flight calibration. In this paper, we shall first demonstrate the high accuracy of such calibrations based on test range data. Later, we shall quantify the geometric accuracy of the IKONOS camera using large IKONOS stereo image blocks with and without ground control, thus validating the exterior and the interior orientation calibrations.

Since its launch in September of 1999, the IKONOS satellite has been consistently providing high quality 1-meter panchromatic and 4-meter multispectral images. Accurate interior and exterior orientation enable IKONOS to achieve high geometric accuracy with or without ground control. Exterior orientation is determined by on-board GPS receivers, star trackers, gyros, and interlock angles. Post-processing of GPS data with software incorporating sophisticated filtering and orbital modeling algorithms results in accurate ephemeris. Kalman filtering of gyro and star tracker data results in optimal combination of lower frequency star tracker attitude data exhibiting high absolute accuracy with high frequency gyro data being very accurate over short time intervals. Interlock angles relate the attitude and the camera coordinate systems and have been calibrated both pre-launch and in-flight. Initial interior orientation parameter values were determined by pre-launch measurements and later refined by in-flight calibration. In this paper, we shall first demonstrate the high accuracy of such calibrations based on test range data. Later, we shall quantify the geometric accuracy of the IKONOS camera using large IKONOS stereo image blocks with and without ground control, thus validating the exterior and the interior orientation calibrations.

IKONOS stereo imagery is particularly well suited for 3-D feature extraction. The sophisticated geometric and radiometric characteristics of the IKONOS sensor provide the end user with excellent metric accuracy and wealth of information which can be used for interpretive analysis. In order to be able to perform stereo feature extraction with sufficient accuracy the very complex IKONOS sensor model needs to be effectively communicated to softcopy photogrammetric software. The Rational Polynomial Camera (RPC) model accomplishes such task with great efficiency and no discernable loss of accuracy. Since the RPC IKONOS model is expressed simply as a ratio of two cubic polynomials it is generic enough to be easily interfaced with most COTS photogrammetric packages. Furthermore, it contains enough degrees of freedom to maintain full accuracy of the physical IKONOS sensor model. The paper demonstrates that the RPC IKONOS model differs by no more than 0.04 pixel from the physical model, with the RMS error below 0.01 pixel.

A new estimator of variance–covariance components is presented. The proposed estimator is derived by applying the principle of maximum-likelihood estimation to the posterior probability density function for the case when no prior information is available.

A new method for the estimation of variance components is presented. The proposed method combines the concept of maximum-likelihood estimation with the Bayesian approach and facilitates computationally efficient introduction of prior information into the estimation process.