Orbiter vehicles are routinely exposed to a variety of small scale debris while operating in low earth orbit. Impacts with such debris often result in surface and/or subsurface damage to orbiter windows. Current procedures require windows to be manually inspected for impact damage after each shuttle mission.
Once identified, surface damage feature depths are determined by analyzing mold impressions of the damaged areas. Subsurface damage always results in window rejection since the depths of subsurface features are deemed  unmeasurable  using standard mold impression measurement techniques.
This paper presents an automated optical technique for measuring the depth of small scale surface and subsurface damage features in orbiter windows. Test results based on actual orbiter window damage features are also presented

Delgado, H. N.

Gleman, S. M.

Thayer, S. W.

Thompson, J. E.

English

Embry-Riddle Aeronautical University

The Space Congress® Proceedings 1994 (31st) Space Exploration and Utilization for the Good of the World Apr 27th, 1:00 PM - 4:00 PM Paper Session II-B - 3-Dimensional Feature Mapping Using Spatial Spectral Analysis S. W. Thayer Kennedy Space Center S. M. Gleman Kennedy Space Center J. E. Thompson Kennedy Space Center H. N. Delgado Kennedy Space Center Follow this and additional works at: https://commons.erau.edu/space-congress-proceedings Scholarly Commons Citation Thayer, S. W.; Gleman, S. M.; Thompson, J. E.; and Delgado, H. N., "Paper Session II-B - 3-Dimensional Feature Mapping Using Spatial Spectral Analysis" (1994). The Space Congress® Proceedings. 10. https://commons.erau.edu/space-congress-proceedings/proceedings-1994-31st/april-27-1994/10 This Event is brought to you for free and open access by the Conferences at Scholarly Commons. It has been accepted for inclusion in The Space Congress® Proceedings by an authorized administrator of Scholarly Commons. For more information, please contact commons@erau.edu. 3-DIMENSIONAL FEATURE MAPPING USING SPATIAL SPECTRAL ANALYSIS 28 February 1994 ABSTRACT S. W. Thayer S.M.Gleman J. E. Thompson J.D. Collins H. N. Delgado Orbiter vehicles are routinely exposed to a variel)' of small scale debris while operating in low eanh orbit. Impacts with such debris often result in surface and/or subsurface damage to orbiter windows. Current procedures require windows to be manually inspected for impact damage aftu each shuttle miuion. Once identified, surface damage feature depths are determined by analyzing mold impressions of the damaged areas. Subsurface damage always results in window rejection since the depths of subsurface features are deemed "unmeasurable" using standard mold impression measuremefll techniques. This paper presents an automated optical technique for measuring the depth of small scale surface and subsurface damage features in orbiter windows. Test results based on actual orbiter window damage features are also presented. 1-NET, lnc. 1-NET, loc. I-NET, Inc. NASAIKSC/DL-FSS-24 NASA/KSCIRM-SAO 5-13 INTRODUCTION The I-NET Special Instrumentation LaOOratory (SPIL) has recently been tasked with developing a system 10 inspect orbiter windows during orbi1cr processing operations at the OPF. The system wilt be required to scan the entire window surface to detect both surface and subsurface impact damage. Once identified, each damage fea1ure will require detailed analysis to determine the depth extent of the damage. Current plans call for the inspection process to be divided into two phases. The first phase will involve scanning the entire window surface to locate all surface and subsurface impact damage features. Tests using a commercially available optical scanner have demonstrated that a CCD line array scanner is capable of detecting both surface and subsurface damage features. A damage feature database will be maintained for each window to ensure that only new damage features are singled out for detailed analysis. The second phase of the inspection process will employ a high resolution CCD camera system to perfonn delailed analysis of the damage features de1ccted during the initial window scan. The principle objective during this phase of the inspection process is 10 de1errniDe the maximum depth extent of the damage features. This paper describes one possible method of making such depth determinations. TECHNICAL APPROACH The spatial spectral analysis technique described here is closely related to the operation of the optical micromeler or refocus microscope. A refOCU:S microscope uses a simple change in object distance 10 measure the depth of surface features. 1be micromeler is placed on the measurement surface and adjusted until the surface area surrounding the feature to be measured is in clear focus. After noting the micromeler setting the instrumen1 is refocused on the deepest portion of the surface feature. The measured depth is then taken as the change in micromeler setting (object distance). Refocus microscopes have traditionally been used 10 measure surface features on opaque materials. 1be instrument can, however, often be used to measure depths of surface and subsurface features in translucent materials. The refocus approach to depth measurement can be largely automated by employing digital imagery and spectral analysis. In essence, a computer can delenn.iDe when an image (or portion thercoO is in focus by examining the spatial frequency components of a series cl objecl plane images. Well focused images are characterized by increased energy in the high frequency components. The points at which an image comes into focus can therefore be determined by examining the relative changes in the spatial spectral characleristics of the object plane image sequence. The basic procedure for determining the topography of surface or subsurface features using spatial spectral analysis is the same. The firs1 step is to digitize a series of images containing the feature to be analyzed. For each image the object plane of the camera system is advanced a small distance into the window malerial. This is accomplished by advancing the position of the camera while maintaining a constant focal length. After the overall image sequence has been digitized, a reference grid, which CO'ICIS the areas ... :~:: . :>.= < : I ' ' ' ' ' o~--Figure 1. Object Plane Grid Geometry of interest, must be established. It is critical to ensure that the reference grid position and orientation are held constant for all images in the sequence. 1be grid cell siz.e is somewhat arbitrary but it should be selected with lateral spatial resolution requirements in mind. If the collection of reference grids for a comple1e sequence of objec1 plane images is thought of as a 3-dimensional structure, then the individual unit of analysis becomes a single grid cell colunm. The basic object plane grid geometry is shown in Fig. I. Note that one of the grid planes must be selected to represent the surface of the window. Spectral analysis may be performed on as many grid cell colunms as deemed necessary to fully evaluate surfaceorsubsurfacefeatureinquestion. In order to analyz.e a grid cell colunm, a two dimensional Fast Fourier Transfonn (FFl) must be performed on each cell of the grid square colunm. Each complex FFT result must be further processed to produce a single statistic or discriminant which accurately describes the "high frequency energy" conlent of the object plane cell in question. The power spectral density function ol each FFT is computed and scaled relative to the grid column maximum. A single "focal energy" statistic is then computed by inlegrating the two dimensional power spectral density function over the frequency range of interest. Once the focal energy statistic has been dclennined for each object plane cell in the grid cell colunm, a profile of focal energy versus grid cell depth is constructed. Points Ill which the image cell is in focus are delennined by searching for maximum values (i.e. peaks) in the focal energy profile. If the feature being analyzed is an open pit (e.g. micomcteoroid crater), the focal energy profile will have one peak representing the pit swface elevation. If, however, a subsurface fracture plane is being analyzed, the focal energy profile may exhibit peaks at both the window surface and the subsulface fracture plane depth. 1Wo important facton must be considered when analyzing focal energy profiles generated from FFI's. First, the algorithm requires identifiable,ligh1scatteringfeaturestofocuson. If any portion of !he pit topography is relatively smooth and clean, !he algorithm will find nothing to focus on. Second, the FFr measures spatial energy over the entire area of the grid cell. This being the case, grid positions at which the local swfacc topography is flat (relative to the object plane) will exhibit sharp, well defined peaks in the focal energy profile. Grid positions at which the local surface topography is relatively steep, on the other hand, will produce wider, flatter peaks, since different parts of the grid cell are in focus at different depths. Finally, an imponant distinction must be made between sulface and subsurface depth observations. Deplhs of subsulface features mus! be corre.cted for refraction. Orbiter windows are composed of fused silica which has a refractive index of about 1.46. INITIAL TEST RES UL TS The first data set presented here was obtained from the I.SOC training window supplied 10 the SPIL for test and evaluation purposes. The actual damage featUJ'C analyzed was a small open sulface pit. The pit, which is shown in Fig. 2. is approximately 0.020- long and 0.008" wide. Fig. 2 also reveals evidence of a subsurface frac1ure in the form of sulface cracks extending out from the ends of the open pit. A series of 26 images, digitized with successively deeper object plane depths, were used to analyze the open surface pit and the adjacent subsurface fracture plane. The complete set of images is provided in Appendix A. The reference grid used to partition !he object images is shown in Fig. 3. A grid cell size of 32 by 32 pixels (approximately o.oor square) was selected. Individual image cells are identified by a pair of indices which specify !he row and column. Cell (8,5), for example, refers to !he 81h row and the 5th column. The object plane dep!h 5-15 Figure 2. Window Surface Pit ranged from 0.001 " above the sulface to 0.004" below !he surface in 0.0002" increments. It should be noted that the actual location of !he surface can be difficult to define. Not readily apparent from Fig. 2 is the fact that a portion of the window material in the impact zone is raised above the actual window surface. For the purposes of this analysis, a local surface reference for use in depth calculations was determined from the image data . itself. In order to compute the surface correction factor, a number of undisturbed areas near the open pit were selected for use as surface reference points. By performing spatial spectral analysis on these areas, the actual elevation of the window surface relative to the grid cell coordinate system was computed. The results of surface reference analysis, which are provided in Appendix B, revealed that the actual window surface is 0.0004" above the grid coordinate reference plane. The first area analyzed was the open surface pit. Twen1ygridcells,representing an area completely enclosed by !he pit perimeter, were analyzed. The 20 focal energy profiles are provide in Appendix C. The maximum observed depth of 0.0016" occurred at grid cell (9,4). The actual maximum depth of o.oor is found by subtracting the 0.0004" surface correction from the observed depth. The focal energy profile for grid cell column (9,4) is shown in Fig. 4. Figure 3. Analysis Grid Geometry The second area of in1eres1 was the subsurface fracture plane which appears to extend outward and downward from the open pit. Grid cell positions at which the fracture plane was clearly visible were somewhat limited due to the surface lighting geometry. In all a total of 12 grid cell columns were analyzed. The complete set of focal energy profiles is provided in Appendix D. The maximum observed depth of 0.0028" occurred at grid cell positions (3,5) and (4,5). The actual maximum depth of 0.004r is found by subtracting the 0.0004" surface correction and multiplying by the 1.46 refractive index. The focal energy profile for grid cell position (3,5) is shown inFig.5. ~ v I\ ... o 40 .:z.o ·1.C o.o 1.0 ~.o -Figure 4. Energy Profth at Grid Cell (9,4) .. ,. Note that the maximum subsurface fracture plane depth found here is not necessarily the deepest pnrt of the fracture plane. It simply represents 1he deepest part of the plane which was visible at the time image sequence was captured. The lighting geometry used for this test was designed primarily to illuminate the open surface pit. A more detailed analysis with improved surface lighting would be required to fully characterize1hesubsurfacefractureplane. FOLLOW-ON TEST RESULTS The second set of data presented here was obtained from a window which was removed from OV-103 just prior to STS-60. The damage feature, which is shown in Fig. 6 through 8, is a nearly circular pit approximately 0.030" in diameter. As with the previously discussed damage feature, sub-surface fracture planes were evident in the glass surrounding the pit. The spatial spectral analysis performed on this damage feature was identical to the procedure described earlier. The depth of the pit, as determined from spatial spectral analysis was 0.0038". 1bis result agrees closely with the depth derived from earlier mold impression mcasunnents. The sub-surface fracture planes surroundingthepitwerenotanalyzed. The depth data derived from this data set was also used to generate a 1hree dimensional representation of the pit topography which is showninFig9. I If "' Figure 5. Energy Profile at Grid Cell (3,5) CONCLUSIONS Determining damage feature depths using spatial spec1ral analysis appears promising. Unlike the current mold impression technique, spatial spectral analysis can be used to analyze subsurface aswellassurfaccfcatures. This analysis used adjacent 32 x 32 pixel (approximately 0.002" square) grid cells and O.ooor objoct plane spacing. Smaller grid cell size or overlapping grid cells could, however, be used to improve lateral resolution. In addition, an optical system with a shorter depth of field could be used in conjunction with closer object plane spacing to improve depth resolution. It is important to remember that this process is inherently limited to identifying and measuring only those features which can be illuminated from 1he surface of the window. If this technique is utilized in a working instrument, camera lighting geometry will become a major area of interest. Figure 9. JD Map of Window Pil Figure 6. Impact Pit (Surface) Figure?. Impact Pil (Mid Depth) Figure 8. Impact Pit (Bottom) ... , 

Paper Session II-B - 3-Dimensional Feature Mapping Using Spatial Spectral Analysis

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Paper Session II-B - 3-Dimensional Feature Mapping Using Spatial Spectral Analysis

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