Geometric Validation of PF-ASAR IMP and IMM Products

Transcription

1 Geometric Validation of PF-ASAR IMP and IMM Products Hannes Raggam, Martina Franke, Wolfgang Hummelbrunner Joanneum Research, Institute of Digital Image Processing Wastiangasse 6, A-8010 Graz Austria Tel: , Fax: hannes.raggam@joanneum.at ABSTRACT As for any other remote sensing data, the geometric fidelity of Envisat ASAR image products is of interest to the user community. The Institute of Digital Image Processing is in charge to check and proof the geometric quality of Envisat products acquired in the ASAR image mode. This paper focusses on results, which have been achieved for a data set comprising IMP and IMM products, which represent the precision and the medium resolution mode, respectively, and which have been acquired over the Flevopolder test site. With regard to geometic validation, the a-priori location accuracy of the Envisat products is of particular relevance. As demonstrated through the validation tests, the ASAR products being investigated in general show a highly acceptable geometric performance and promise to be valuable fundamentals for geo-scientific applications. 1 INTRODUCTION Since March 2002 Envisat products are acquired from the ASAR instrument in image and alternating polarization mode. With respect to the utilization of such products in geo-scientific applications, their geometric quality is of serious interest. Various test sites were specified for the calibration and validation of the geometric quality of ASAR products, providing well known geolocation conditions and reference data. The Institute of Digital Image Processing among other partners is in charge for the validation of the geometric quality of ASAR products acquired in image mode (IM). Early results achieved for the very first IM products have been reported in [1] and [2]. Occasionally, these have shown up minor processing and data errors along with the necessity of error removal. The ASAR instrument and the SAR processor were accordingly changed and upgraded during this phase ([3]). In autumn 2002, a continuous test data set of ASAR products was acquired over the test site Flevopolder (The Netherlands). This data set comprises 8 acquisitions at time intervals of a few days only and for the first time enabled systematic tests with regard to the geometric fidelity of the Envisat ASAR sensor and the performance of the processor being used, i.e. PF-ASAR version Tests have been carried out on products acquired in any imaging mode, as there are the IMS (single look complex format), IMP (precision mode), IMG (ellipsoid geocoded) and IMM (medium resolution) imaging mode. In this paper, however, only the results achieved for the IMP and IMM products are presented, while the IMS and IMG products are treated by other partners, namely the German Aerospace Center (DLR) and the Remote Sensing Laboratories (RSL) in Zürich. The paper first gives a background on the validation activities with respect to software tools, test data and algorithms, and then gives a summary of validation results achieved for the IMP and IMM products of the above mentioned test data set. 2 VALIDATION BACKGROUND 2.1 Validation Tools The following software tools are used for the calibration and validation of Envisat products: The self-developed Remote Sensing Software Package Graz (RSG), which is designed for geometric processing of remote sensing image data. This software was extended with regard to the treatment of Envisat ASAR products and is used for Proc. of Envisat Validation Workshop, Frascati, Italy, 9 13 December 2002 (ESA SP-531, August 2003)

2 - the import of Envisat ASAR data - quality analysis based on point residuals - optimisation of sensor parameters, like orbit, range or timing parameters The image processing software system Erdas Imagine, which is used - for image display (visual product format verification) and - for the measurement of control points The ESA software tool EnviView, which is used for complementary inspection and verification of image parameters 2.2 Data Set and Reference Data A continuous set of Envisat IMP and IMM products, which was acquired in late autumn 2002 over the Flevopolder test site (The Netherlands), was used for the validation tests described in this paper. The image acquisition dates are summarized in Table 1. As shown, these data are acquired at different beams, i.e. different off-nadir look angles and from ascending as well as descending orbits. All of them were processed with the same version of the ASAR proceesor, i.e. PF-ASAR For the Flevopolder test site, a pool of control points 49 alltogether - was prepared for the calibration and validation of the ASAR products. These are clearly identifyable features like bridges, road crossings etc., for which the ground coordinates related to the Dutch Stereographic projection were measured from topographic maps at a scale of 1 : For a product to be validated, these points have to be measured in the respective image as well. For a selected IMP product the distribution of the control points is shown in Figure 1. Beside ground control points, also 3 transponders were available during the period of data acquisition for this test site. However, for consistency reasons these were not used, because they are hardly visible and not cleary identifyable in the medium resolution products. It should be noted at this point, that the nominal pixel size for the IMP products is 12.5 meters, while this is only 75 meters for the IMM product. Date Orbit Swath 18/10/ IS7 / DSC 21/10/ IS5 / DSC 24/10/ IS4 / DSC 27/10/ IS5 / ASC 30/10/ IS6 / ASC 02/11/ IS1 / DSC 03/11/ IS7 / DSC 03/11/ IS1 / ASC Table 1: Acquisition dates of Envisat validation test data Figure 1: Flevopolder IMP product and control points 2.3 Validation objectives and methods Data Import In a first preparatory step the image data have to be imported, i.e. the native Envisat data format is converted to a known image format being used by image display systems, in order to visualize the image (visal quality check). Besides, various sensor-specific parameters need to be read from the header records accompanying the image product (format verification). These are further used to estalish a sensor model, which allows to transform a target point given on ground to the image or vice versa.

3 2.3.2 A-priori location accuracy Given a number of control points, point residuals are calculated for each control point in 2 ways: 1. using a backward (ground-to-image) geocoding algorithm in order to transform the East, North, Height ground coordinates of a control point to corresponding image pixel coordinates. The coordinate differences (residuals) between these resected coordinates and the measured pixel coordinates serve to evaluate the location accuracy of the scene in image pixel units. 2. using a forward (image-to-ground) geocoding algorithm in order to transform the Azimuth, Range pixel coordinates measured in the image to corresponding East, North, Height ground coordinates. This is achieved through intersection of the range circle with an ellipsoid surface being co-centric to the Earth ellipsoid, which is achieved through enlargement of the Earth ellipsoid s axes by the height of the target point (see Figure 2). The coordinate differences between the intersected coordinates and the measured ground coordinates lead to a location accuracy expressed in meters on ground. It should be noted, that the real location error is achieved through intersection of the range circle with the real terrain surface as represented e.g. by a digital terrain model. The basic equations being used in both approaches are the SAR Doppler and range equation. More details on the algorithmic backgrounds of SAR image quality analysis as used at the Institute of Digital Image Processing can be found in [4] and [5]. Figure 2: Forward geocoding to determine localization errors Geocoding Another option to determine the location accuracy of a scene is given through geocoding. Depending upon whether a DEM is available or not we use to distinguish between terrain and ellipsoid geocoding. For flat areas like Flevopolder, representative location accuracy measures may be achieved through ellipsoid geocoding, where the terrain surface is neglected. In general, the comparison of target point coordinates extracted from the geocoded scene with the given ground coordinates leads to equivalent accuracy measures like the method sketched above under item 2. Other calibration and validation objectives to be covered through geocoding are to measure and validate the swath width of a product to verify the geometric continuity of IMM products consisting of multiple image slices visualization of location accuracy through overlay of geocoded products with each other or with reference information Least-squares parameter adjustment Least-squares parameter adjustment techniques may be used to optimize selected parameters of the sensor model in order to improve the location accuracy of the respective scene, which is then addressed as a-posteriori accuracy.

4 These techniques are based on the use of ground control points and linearization of the SAR Doppler and range equations. Starting from initial values for the sensor model parameters, as they are given in the header files of Envisat products, the least-squares procedure iterates these parameters in order to find their optimum values. An overall criterion for the least-squares parameter adjustement is the minimization of the point residuals. Hence, the absolute location accuracy of the scene is optimized in that way. Moreover, the adjustment gives a feedback on the quality of certain parameters of the sensor model. Conclusions on their initial quality can be made, depending upon the degree of modification of the parameters. 3 VALIDATION OF IMP PRODUCTS 3.1 Evaluation of A-priori Location Accuracy For the IMP products being acquired as listed in Table 1 first the a-priori location accuracy was evaluated. Therefore, the control points first had to be measured in the images and subsequently the location accuracy of each individual point was determined following the procedure described in section Overall point residual statistics, in particular mean values and standard deviations, then serve to conclude on representative accuracy measures. For the IMP products, these values are summarized in Table 2, which gives the point residuals in image and on ground, i.e. in azimuth and range pixel units as well as in meters in East and North. The following accuracy statements can be made: In comparison to others, the scene acquired from orbit 3311 shows a relatively large shift in azimuth direction, expressed by a mean point residual value of 15.6 pixels, which is equivalent to almost 200 meters on ground. In general, however, the a-priori location accuracy is rather good. Mean residuals values, expressing the location shift of a scene use to be less than 10 pixels, or less than 100 meters on ground for the majority of the products. In range direction, a systematic bias can be detected, which is manifested through the mean pixel residuals being negative for all of the investigated products. This range bias is even larger in the scenes acquired from orbits 3526 and A similar, but less distinct effect is to be noticed for the mean azimuth residuals. Orbit GCPs Mean Std.Dev. Mean Std.Dev. Az Rg Az Rg East North East North Table 2: Residual statistics of IMP products 3.2 Ellipsoid Geocoding Ellipsoid geocoded products represent an Envisat standard image mode product, being denoted as IMG. On the other hand, ellipsoid geocoding is used for IMP, IMM, or IMS products to validate the swath width of an Envisat product or to visualize their location accuracy. Figure 3 shows a superposition of an IMG product (orbit 3311, shown in red) and an ellipsoid geocoded IMP product as generated in the course of the validation tests (green). No location diffeences are visible, while radiometric differences may be due to different pixel size and resamplig technique being applied. The products hence are equivalent from a geometric point of view.

5 A superposition of ellipsoid geocoded scenes, as generated from the IMP products acquired from orbits 3311 (red), 3354 (green) and 3397 (blue), is shown in Figure 4 and gives an impression regarding the relative location accuracy of these scenes. The 3311 scene is shifted relatively to the others in North-Soth direction due to its azimuth shift mentioned above, while the shift in East-West is about the same for the three scenes. Figure 3: Superposition of IMG product (R) and ellipsoid geocoded IMP products (G). Left: Full scene; Right: Closeup Figure 4: Superposition of ellipsoid geocoded IMP products.

6 3.3 Least-squares Parameter Refinement The least-squares parameter adjustment was applied to the IMP products. Figure 5 shows a comparison of ellipsoid geocoded IMP products before (a-priori, left) and after (a-posteriori, right) parameter refinement. The images show the scenes acquired from orbits 3311, 3354 and 3397 in an RGB presentation, where the left illustration is a closeup of Figure 4, while the right illustration presents the same area after parameter refinement. The removal of the azimuth shift of scene 3311 is obvious and the scenes can be overlaid after parameter refinement more or less with pixel accuracy. Figure 5: Superposition of ellipsoid geocoded products before (left) and after (right) least squares parameter adjustement. 4 VALIDATION OF IMM PRODUCTS 4.1 Algorithmic Issues Treatment of Multiple Image Slices Envisat IMM products may be split into seperated image slices, covering part of the very long image strips. In such cases the continuity of these individual slices is a first issue to be investigated. The slices are first read as individual images with their inherent sensor parameters, and these slices as well as their sensor parameters are then merged to a single continuous image data set (see Figure 6). This allows an immediate visual check of the continuity of the individual image slices Treatment of Multiple SRGR Updates Envisat IMM products are delivered in ground range presentation. Ground range image pixels can be converted to corresponding slant range distances in meters using so-called SRGR polynomials, which are polynomials of 4 th order in case of Envisat. For IMM products, which in general represent long image strips extending over several hundreds of kilometers, such SRGR polynomials are many times updated along the image strip, i.e. given at selected line intervals, in order to compensate for Earth rotation effects during image acquisition. Thus, a time dependency is induced for the conversion of ground range pixels to slant range distances. Investigations of the characteristics of this time dependency lead to the conclusion, that the multiple SRGR updates can be modelled by a linear function in azimuth. The following relationship therefore can be established to convert ground range pixels (rg) at a given image line/azimuth coordinate (az) to slant range distances (r s ) for IMM products: r s = a 2 3 ( b + b rg + b rg + b rg + b 4 ) a1 rg + a2 rg + a3 rg + a4 rg + az rg The coefficients a i and b i are derived from the discrete SRGR updates given in the geolocation record of the data.

7 Figure 6: Individual (top) and merged (bottom) image slices of IMM product. 4.2 Evaluation of A-priori Location Accuracy The location accuracy of the IMM products was determined in the same way like described for the IMP products. The mean values and standard deviations of the point residuals achieved in Azimuth/Range and East/North, respectively, are summarized in Table 3. The following accuracy statements can be made: Again, all of the scenes show a bias in range, as all the mean range residual values are negative. Although difficult to compare due to the different pixel resolution, the scene acquired from orbit 3311 does not have an azimuth shift in the IMM mode equivalent to the one in the IMP product. On the other hand, the scenes acquired from orbits 3526 and 3540 show a significantly larger range bias than the others, which transforms to a metric equivalent of more than 200 meters in East. These 2 scenes acquired from orbits 3526 and 3540, however, have a smaller overlap with the core test site than others and hence less of the preselected control points could be measured. Still it is unclear why and wherefrom a difference in location accuracy between IMP and IMM products can exist and originate, respectively. Orbit GCPs Mean Std.Dev. Mean Std.Dev. Az Rg Az Rg East North East North Table 3: Residual statistics of IMM products

8 4.3 Ellipsoid Geocoding The IMM products were ellipsoid geocoded in order to check their geometric continuity and to measure the swath width of the products. The ellipsoid geocoding is done seperately for the individual image slices as well as for the entire image strip being merged during data import. In case that the individual image slices are continuous and consistent in their geometry, the ellipsoid geocoded products should then fit together from a geometric point of view. This is illustrated in Figure 7 displaying the ellipsoid geocoded products of the entire IMM strip (left) as well as the 2 separate image slices (mid) and a closeup of the border area between the individual slices (right). The Figure proofs that the images fit from geometric point of view. However, the seperately displayed slices are radiometrically different due to their different image histograms and because intentionally no radiometric adaptation was made. Figure 7: Ellipsoid geocoded image slices of IMM product (orbit 3397). Left: Full image strip; Mid: Individual slices; Right: Closeup Figure 8 illustrates a superposition of ellipsoid geocoded IMM products scenes acquired from orbits 3311 (red), 3354 (green) and 3397 (blue). As can be confirmed through the numbers given in Table 3, these products match more or less with sub-pixel accuracy, as the mean point residuals as well as the standard deviations of the residuals are very similar, showing differences in the sub-pixel range only.

9 Figure 8: Superposition of ellipsoid geocoded IMM products. 5 SUMMARY The paper gives a concise summary on validation tests applied to a continuous test data set of ASAR products acquired in IMP and IMM mode and a discussion of the achieved results. The following conclusions can be drawn: Suitable software tools and algorithms to validate the geometric quality of ASAR products are available. Where necessary, these have been appropriately adapted to the requirements arising from the ASAR products and their native data formats. For instance, importer software had to be extended to the specific Envisat data format, including the image data as well as header information. Whenever the importer software successfully reads image as well as header parameters being requested, it can be assumed that the respective Envisat product passed the format verification test from a geometric point of view. This happened for all of the investigated Envisat products. The multiple SRGR parameter updates being relevant for IMM products are properly treated through the implementation of a continuous 2-dimensional polynomial function. The coefficients of this function also can be subjected to the least-squares parameter adjustment procedure. A systematic range bias is inherent to the Envisat image mode products, which have been generated by the PF- ASAR 3.03 processor. Occasionally, ASAR products show unexplainable location errors when compared to those achieved for other scenes. For this test data, this happened for an IMP product showing a relatively large azimuth shift and for 2 IMM products, showing a significantly larger range bias than others. In general, however, the a-priori location accuracy of IMP products can be specified to be in the range of a few image pixels, while for the IMM products an a-priori location accuracy in the sub-pixel range is feasible. If sensor model parameters are optimized using least-squares adjustment techniques, which leads for instance to a removal of the range bias etc., sub-pixel accuracy becomes feasible for IMP products as well. The validation tests, however, shall be completed and the achieved results still need to be verified in more detail. Moreover, the results shall be cross-checked with those achieved from other investigators.

10 Follow-on validation activities should be extended from a temporal as well as from a spatial point of view. I.e. data acquisitions over a longer period of time on the one hand, and data acquisitions over other test sites, including hilly and mountainous terrain, on the other hand shall be included to ensure a comprehensive validation of the geometric quality of Envisat products. 6 REFERENCES [1] D. Kosmann, J. Holzner, W. Hummelbrunner, D. Small (2002): Geometric Accuracy of ASAR Products Calibration Phase. Proceedings EUSAR 2002, Cologne, Germany, June 4-6, 2002, pp [2] D. Small, D. Kosmann, J. Holzner, H. Raggam, M. Pirri, A. Schubert, U. Kruettli, W. Hummelbrunner, M. Franke (2002): ASAR Level 1 Geolocation. Envisat Calibration Review, ESTEC Noordwijk, The Netherlands, September [3] ASAR Cal/Val Team (2002): ASAR Status Update. Technical Note, Issue 1, Revision 0. [4] H. Raggam (1990): Analytical Simulation for Quality Control of Geocoded SAR Images. Internal Technical Note, Institute of Digital Image Processing, Joanneum Research, Graz, July [5] A. Sowter, D.J. Smith, J.E. Laycock, J. Raggam, D. Strobl, and G. Triebnig (1990): Study of an Error Budget for ERS-1 SAR Imagery. Final report to ESA contract no. 7689/88/HE, GEC Marconi Research Centre.