REPLACEMENT SENSOR MODEL (RSM) PERFORMANCE ... - asprs

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RSM SUPPORT DATA GENERATION RSM Generator Introduction As described in the companion paper, the RSM Generator module is a software development kit that can be integrated into any application. The application provides the original, rigorous sensor model to the RSM Generator through a standard Application Program Interface (API) and the RSM Generator uses it to create the RSMSD. In these studies, RSM Generator 2.2 was integrated into BAE Systems rapid prototyping environment, in which many commercial sensor models are available via BAE Systems Socet Set® software libraries. * No modifications to the RSM 2.2 code were needed to accomplish these new studies. The rapid prototyping environment used in the RSM performance studies also contains BAE Systems Multi- Image Geopositioning (MIG) service, which performs monoscopic, stereoscopic, and multi-image target extraction and triangulation with sensor models and Digital Elevation Models (DEMs). In order for the MIG service to use the RSMSD, RSM Exploiter 2.2 is also integrated into the environment. The RSM Exploiter parses the RSMSD and delivers sensor model functionality through the same API that is used for the original, rigorous sensor model. Thus, in addition to testing the RSM performance, the studies also demonstrate the capability of the RSMSD format to communicate all of the information needed to exploit RSM. The RSM Generator begins its processing by obtaining the original sensor type from the sensor model and looking up any fitting strategies that have been configured for that type. If there are none, then it uses the configured default strategies. Each strategy has an associated priority order, and if the first strategy results in a fit that doesn't meet the criteria, then the next strategy is tried. Typically one of the first strategies selected meets the fit criteria, but if necessary the RSM Generator will try all of the sensor-specific strategies followed by all of the default strategies until it finds a fit that meets the criteria. If all of the strategies are exhausted, then the RSM Generator will return the best fit and alert the calling application that the criteria were not met. The strategy-driven logic of the RSM Generator is diagrammed in Figure 1. The same logic is used in both major operations of the RSM Generator: projective model fitting, and adjustable parameter selection/error covariance mapping. Start Save No No Usable? Yes Best so far? Yes No Get next strategy? Fit using strategy Compute metrics Pass? Yes Return this fit Yes Figure 1. RSM Generator logic. No more ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2, 2008 Return best saved fit (if any) The sensor-specific and default strategies are configurable in each RSM Generator installation. On initial installation, the sensor-specific strategies are those that have been identified by BAE Systems in previous performance studies as having the best performance. In addition, the default strategies have been researched carefully, such that on initial installation the default strategies alone give good performance for every sensor that has been studied by BAE Systems. This gives the integrator confidence that the RSM Generator is likely to perform well even for a new sensor type that has not participated in previous performance studies. * Socet Set is a registered trademark of BAE Systems.
For the commercial satellite study being reported in the paper, the first tests were conducted without any sensorspecific strategies for QuickBird or WorldView. Following the procedures outlined in the RSM Generator Users Manual, the RSM generation metrics were reviewed for the first few images, and sensor-specific strategies that are minor departures from the defaults were created in order to arrive immediately at the best strategy. This configuration process required only a short time, and did not require any software modifications. Once complete, the configuration was used successfully for all of the images. Projective Model Fitting The primary elements of a projective model fitting strategy are whether to use rational polynomials for the ground-to-image function, and if so then how many sections will be used to cover the image, and which terms (coefficients) will be used in the numerator and denominator for the row and column functions, and whether to use an interpolating grid, either by itself or as a correction to the rational polynomials. Along with those primary elements, the strategy also specifies what ground coordinate system is to be used for the polynomials and/or grid, and how the fitting is to be done. The projective model fitting accuracy is evaluated by the RSM Generator as part of the fitting process. Since the fitting process typically uses the original sensor model ground-to-image function output on a regular 3dimensional grid, the evaluation is usually done by calling the original sensor model ground-to-image function at an additional 1000 points generated randomly in the image domain and height range of interest. It is these randomly selected evaluation points whose statistics will be considered below. A test set of 58 QuickBird 2 images and 4 WorldView 1 images were used to test the RSM Generator. The images were made available as Basic Products with rigorous sensor model support data. BAE Systems Socet Set was used to import the support data into the testbed, and Socet Set libraries provided the rigorous sensor model functionality in the testbed. Past testing with line-scanning sensors indicated that a polynomial with a correction grid would be a good choice for the RSM projective model for these sensors, and a few quick experiments confirmed this and provided a specific strategy. The coordinate system used is a rectangular system with the Z-axis along a nominal line of sight and the X-axis nominally aligned with the scan direction. The baseline polynomial is the same for both image row and image column. It has a numerator with terms up to fifth order in X and Y, and up to third order in Z, with cross terms up to a maximum combined order of 5. It has a denominator with terms of first order only (no cross terms). The same strategy is used for both QuickBird and WorldView with one exception. It was found that in order to keep the maximum RSM fitting error below 0.05 pixel required a finer grid-spacing in the X-direction for WorldView. The projective model fitting is summarized in Table 1. The "typical RMS fit error" is the mean of the RMS combined fit errors for all images. The "maximum fit error" is the largest combined (row and column) residual error for any point in any image. The "maximum grid spacing" is a strategy parameter such that the actual grid spacing is smaller in every image. The "typical grid spacing" is the mean for all of the images. The support data size per image is displayed as a range from the smallest to the largest. Table 1. Projective model statistics for QuickBird and WorldView RSM projective model fitting statistic QuickBird WorldView Number of images 58 4 Number of image rows 25956 - 37120 57344 - 79872 Number of image columns 27552 35840 Typical RMS fit error 0.002 pixel 0.001 pixel Maximum fit error 0.040 pixel 0.049 pixel Maximum/Typical grid spacing-row direction 400 m / 250 m 100 m / 60 m Maximum/Typical grid spacing--column 2000 m / 1300 m 2000 m / 1100 m direction Grid spacing--LOS direction 1000 m 1000 m Original support data size 0.41 - 1.22 Mbyte 0.51 - 0.57 Mbyte RSM support data size 0.06 - 0.12 Mbyte 0.87 - 1.11 Mbyte The average time required to generate the RSM support data in these experiments was about 1.7 s per image for QuickBird and 12 s per image for WorldView, and the time also includes adjustable parameter selection and error ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2, 2008
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REPLACEMENT SENSOR MODEL (RSM) PERFORMANCE ... - asprs

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