CALIBRATION PROCEDURES OF THE IMAGING LASER ALTIMETER AND DATA PROCESSING

Transcription

1 CALIBRATION PROCEDURES OF THE IMAGING LASER ALTIMETER AND DATA PROCESSING Karl-Heinz Thiel, Aloysius Wehr Institut für Navigation, Universität Stuttgart Geschwister-Scholl-Str. 24D D Stuttgart KEYWORDS: Laseraltimeter, Laserscanner, Imaging, Calibration, DEM, Filtering, Intensity Data ABSTRACT The Institute of Navigation of the University of Stuttgart developed an airborne Scanning Laser Altitude and Reflectance Sensor (ScaLARS). This sensor has been flown in several flight campaigns in Germany and Switzerland. ScaLARS works with a continuous wave semiconductor laserdiode which is intensity modulated with sidetones for ranging. Due to the amplitude modulation scheme it is possible to detect the laser light backscattered from Earth surface without background light interference. Therefore, after flight there are 3D-images available which can be evaluated by using the 3D-information and the intensity data respectively. After explaining the design and the functioning of ScaLARS operational calibration procedures are presented which regard the special geometry of the Palmer scanning pattern. Then the key processing steps are explained for obtaining 3D-coordinates and digital surface models respectively. user obtains a 3D-image of the Earth s surface. Regarding in addition the special geometry of the used Palmer scanning pattern advanced calibration procedures can be applied which will be described and discussed in the following. For a better understanding of the presented calibration and data processing procedures first the functioning of the imaging laser altimeter will be explained. 2. IMAGING LASER ALTIMETER The imaging laser altimeter of the INS is called Scanning Laser Altitude and Reflectance Sensor (ScaLARS). Accordingly to the block diagram of Figure 1 the drive current of the cw-diode laser is modulated by two ranging signals of 1 MHz and 10 MHz respectively. Modulating the drive current causes an intensity modulation of the laser light The 1 MHz ranging tone determines the maximum un- 1. INTRODUCTION Airborne imaging laser altimetry is a new technology to measure directly both Earth s surface in three dimensions and the intensity of the backscattered laser light. The first commercially available laser scanners obtained only range images. The range was derived from the travelling time of the laser light pulse. Using this measurement principle a high technical effort was required to determine the backscattered laser light without background light interference. Therefore, the Institute of Navigation (INS) decided to develop a laser scanner which uses a continuous wave (cw) semiconductor laserdiode. Employing a cw-ranging principle offers the advantage of obtaining high ranging accuracy by using moderate low average optical transmitting power. In addition the intensity of the backscattered laser light is determined without background light interference by using synchronous demodulation techniques. Evaluating both the 3D-information and the intensity information the Figure 1. Block Diagram of ScaLARS Figure 1. Block Diagram of ScaLARS ambiguous range which is 150 m and the 10 MHz tone defines the achievable accuracy which is better than 10 cm. The light is relayed by the scanning mirror to the earth surface. The backscattered laser light is again deflected into the receiving optics by the scanning mirror. The received light is imaged on an avalanch photodiode which converts the optical signal into an electrical one. Now, the phase differences φ 1 of the 1 MHz-tone and φ 2 of the 10

2 MHz between the transmitted signal and the received signal can be measured. The phase difference is directly proportional to the slant range. Figure 2. Scanning Pattern of ScaLARS Table 1. Technical Data of ScaLARS laser source detector average emitted opt. radiation laser wavelength beam divergence (IFOV) max. slant range at 20% reflectivity sample spot diameter at flying height h = 650 m ranging frequencies (modulation) standard deviation (slant range) sample rate dynamic range scanrate adjustable FOV in flight direction across flight direction swath width at (h = 700 m / ±13.6 deg) (h = 650 m / ±19.0 deg) max. distance between adjacent samples in flight direction at 60 m/s across flight direction at h = 650 m intensity measurement CW laser diode Si avalanche photodiode (APD) 0.8 W 810 nm 1 mrad 750 m 0.65 m 1 MHz,, 10 MHz 0.04 m 0.17 m 7.5 khz 50 db max. 20 Hz ±9.7 deg / ±13.4 deg ±13.6 deg / ±19.0 deg 338 m 448 m 3 m 2.4 m 13 bit In addition the intensity I is detected and measured by synchronous demodulation with a 13 bit resolution. For each measurement point on earth the slant range, the intensity and the instantaneous scanning angle are stored. If the instantaneous position and orientation of the laser scanner is known the measurement point can be geocoded by straight forward transformations. The position and the orientation angles are measured by a position and orientation system (POS) which comprises a differential GPS (DGPS) and an Inertial Navigation System. The ScaLARS samples the ranging measurements with a rate of 7.5 khz along the elliptical scanning pattern. In accordance to Figure 2 a surface coverage is achieved by the forward movement of the airplane. The most recent technical data are compiled in Table 1. Before digital elevation models can be computed the laser scanner data must be calibrated. This means ScaLARS s position and orientation relative to POS must be determined. The lateral displacements are not so critical, because they are independent of the flying altitude. The orientation angles must be determined better than a hundredth of a degree. In the next chapter it will be shown, how the redundant information of the Palmer scanning pattern can be exploited for calibration. After calibration the laser scanner measurements can be geocoded and map projections can be carried out. Due to the high point density one obtains directly digital surface models (DSM). For digital elevation models (DEM) additional filtering algorithms are required. They are discussed in chapter 4. 3 CALIBRATION OF THE IMAGING LASER ALTIMETER The laser scanner data exhibit only the slant range between laser scanner and the illuminated point on Earth and the instantaneous direction. The slant range vector s L can be described by this information in a laser scanner coordinate system L. To obtain the elevation of the laser measurement point on Earth the position and the orientation of the laser scanner in respect to an earth fixed coordinate system must be known. The laser measurement point r s_wgs in the earth fixed coordinate system WGS84 can be calculated straight forward by: r s_wgs = r GPS_WGS + [ I-L s L ] ( ) ρ ρ + ( ) WGS I L_I GPS_I (1) r GPS_WGS is the position determined by differential GPS (DGPS) described in the coordinate system WGS84. ρ L_I is the displacement vector between the inertial measurement unit (IMU) and the laser scanner both mounted in the airplane. ρ GPS_I is the translation vector between GPS-receiving antenna

3 scanning direction flight direction Figure 3. Palmer Scanning Pattern and IMU. Both displacement vectors are described in the IMU coordinate system. The vectors ρ L_I and ρ GPS_I have to be surveyed before flight. They can be determined with required accuracy by conventional means. The transform matrix ( ) WGS-I contains the orientation angles yaw, pitch and roll. These angles are measured continuously during flight by IMU. This matrix carries out the coordinate transformation from the coordinate system of IMU into WGS84. Matrix ( ) I-L transforms the slant range vector measured in the coordinate system of the laser scanner into the IMU coordinate system. ( ) I-L contains the Euler angles describing the orientation of the laser scanner with respect to IMU. These so called mounting angles can only be estimated before the flight. They are determined offline with the required accuracy after the mission. The displacement vectors are not critical for the final measurement result, because they are independent of the flying height. The orientation angles are most critical, because they are multiplied by the slant range. The computation of elevation points on Earth is only straight forward, if the orientation is measured by IMU with an accuracy better than 0.01 degrees and the position accuracy is about 10 dm. This means, the mounting angles should be calibrated with an accuracy better than 0.01 degrees. ScaLARS uses a Palmer scanner, which produces an elliptical scanning pattern on ground. Figure 3 shows the resulting pattern if the airplane is moving. It can be seen clearly that if the airplane translates in flight direction almost the same point on Earth is scanned again after a certain time: First time with the part of the ellipse which is located in front of the major axis of the ellipse (forward scan) and a second time with the part of the ellipse which is located behind the major axis of the elliptical pattern (backward scan). This redundant information of seeing the same point from a forward and backward look direction can well be exploited for calibration. 3.1 Solving Height Ambiguity In a first step the correct height interval has to be chosen. The start value from the flight plan can be wrong, producing a incorrect 150m interval for the range measurement. This can easily be detected by visual inspection of the intensity image. Correct range values places objects, like a building at the same position in the overlapping area of the forward and backward scan. Wrong range values results in two images of the object. The image of the backward scan is shifted forward in flight direction, if one interval is missing. Using this information the correct range interval can be chosen. This feature allows to use 150m range ambiguity and even a smaller value would be possible 3.2 Calibration of Pitch Angle Offsets A pitch angle offset dν shifts the scanning pattern either in flight direction or backward, lifting one side and lowering the opposite at the center line. According to Figure 4 this offset leads to an elevation error dh. dh can be determined with a d B flight direction high sensitivity if the forward and backward scans are evaluated for the same ground points in flat terrain. If d is the diameter of the ellipse in flight direction, the offset pitch angle dν is calculated by dh dν = arcsin (2) d In the upper right diagram of Figure 5 the elevation differences dh between forward and backward scans are plotted for an uncorrected pitch offset of 2deg. The lower right diagram of Figure 5 shows the calibrated result. The actual cut for the diagram plot is shown in the laser intensity image which is positioned left in Figure 5. d pitch angle offset dν Figure 4. Pitch Offset Angle dh A

4 Figure 5. Calibration of Pitch Offset 3.3 Calibration of Heading Angle Offsets Figure 6 depicts the geometric configuration to determine heading angle offsets. An error in heading causes a twist between backward and forward scans. This can be directly observed by the intensity image evaluated separately for the forward and the backward scan (see Figure 7). heading offset forward scan dd backward scan Figure 7. Identification of Heading Offset The heading offset dα identified in Figure 8 can be calculated by Figure 6. Heading Angle Offset dd d α = (3) 2b

5 if dd is the observed displacement at the nadir line and b is the semi-minor axis of the ellipse. 3.4 Calibration of Roll Angle Off-Sets The roll angle offsets can be optimally calibrated by flying two overlapping stripes in opposite flight directions (see Figure 8). In this case the tilt of both stripes is a direct measure for the roll offset, if flat terrain is surveyed. However, a more advanced and generally applicable method was developed on the basis of the intensity information. In case of a correct roll angle offset objects like houses or house corners, which can be identified easily in the intensity laser data, must be found at exactly the same position in the overlapping area. With a roll angle offset is a displacement dd R is observed in the intensity laser images. With regard to the center point between the displaced laser points the roll offset dβ can be determined by dd dβ = arcsin R (4) 2 R if R is the slant range measured by the laser. γ flight direction dβ dd R classification of the laser data into the following classes: vegetation, houses and man-made objects. With the input data sets POS-data, laser slant ranges with the scanning angles and the calibration data the geocoded position of the laser points can be calculated straight forward. As soon as all laser measurement points are projected into a map e.g. UTM, raster images can be computed containing either the elevation or the intensity as image information. Depending on the dimensions of the used raster and the density of the laser points more than one point may be within one raster cell. Already in this processing step a prefiltering for identifying ground points is possible. However, commercial image processing programs only process raster data efficiently. Therefore, the presented filter algorithms are applied only on rasterized data. In the following some new algorithms are tested exploiting the comprehensive information content of imaging ScaLARS. They are based on standard remote sensing filtering and classification procedures on pixel and multi-pixel entities. 4.2 Derivation of a Reference Digital Elevation Model In order to identify elements which do not belong to the DEM first a reference digital elevation model (RDEM) has to be derived from the DSM given by the laser scanner data. For this purpose first the laser scanner data have to be projected into a map e.g. UTM and rasterized with a raster spacing large enough so that more than one measured value is in each raster cell. The lines and rows of the raster image are approximated by polynomials. The a Figure 8. Roll Angle Offsets 4 CALCULATION OF DIGITAL ELEVATION MODELS The main objective of current laser scanner surveys is the derivation of DEMs. As laser scanners measure DSMs, filtering out ground points is the most challenging task in computation of DEMs. Several algorithms for this purpose have been developed during the last years (Lindenberger, 1993 and Kraus, 1998). These are e.g. morphological filtering and the application of autoregressive integrated processes. This problem is not yet solved completely. Often manual interaction is required. In addition more and more customers ask for a Figure 9: DSM derived from ScaLARS

6 4.3 Detecting and Identifying Surface Objects Regarding Figure 11 one may conclude that all pixels having elevation above RDEM are objects. However, using this simple algorithm railway embankments are classified as objects. As they are part of the DEM they must be added to the RDEM to obtain a valid DEM. At present such tasks are carried out manually. Figure 10: RDEM derived from ScaLARS coefficients were determined by a best fit over a certain number of raster cells. The number of cells and the polynomial degree is changed with respect to the surface roughness. The coefficients are determined iteratively. In each iteration step only measurements are regarded which are identified as the most possible ground points with respect to the instantaneous RDEM defined by the instantaneous polynomial. Figure 9 shows a typical DSM. The trees and houses can be seen clearly. In Figure 10 the derived RDEM is depicted. Even in dense forest areas the ground is well approximated. However, a quantitative analysis is very difficult to carry out, because in most cases ground truth data are not available with the required accuracy. The differences between DSM and RDEM are shown in Figure 11. Figure 11 makes clear that the RDEM is very close to the DEM. To distinguish between vegetation and nonvegetation the intensity information of ScaLARS can be exploited. As vegetation has an average reflectivity of about 60% in the near infrared (810 nm) and buildings only reflect less than 30% a ternary object mask from the reflectance data is used to perform the classification (s. Figure 12). This method is only applicable, if surveying data are available from seasons with high vegetation. 4. CONCLUSIONS The presented calibration algorithms and the post processing programs for calculating geocoded laser points as well as DSM and RDEM wereapplied for extended data sets obtained during surveys in coastal areas, in Thuringia Germany, where 1000 km² were scanned, and in Switzerland, where glaciers in the Alps were digitized. DSMs could be calculated straight forward. On the basis of RDEMs derived from the laser data, DGMs were computed with minor manual interactions. Very difficult e.g. is the decision, if railway tracks are man-made objects or natural topography. Here the operator has to supervise the decision process. In thick forests the possibility of hitting ground points is very low. In this case the DEM is not reliable anymore. Wrong decisions are possible, due to the lack of ground points. Working with ScaLARS intensity data the calibration and quality check is improved, because an additional video control is not required. Using laser scanner data gathered during summer a classification between vegetation and non vegetation can be carried out. 5. ACKNOWLEDGEMENTS The authors would like to express their gratitude to Martin Thomas for generating ScaLARS intensity images and plotting the diagrams in chapter 3. Figure 11: Difference between DSM and RDEM

7 Figure 12. Ternary Object Mask from Reflectance Data 6. REFERENCES Ch. Hug, "Urban Topography Survey with the Scanning Laser Altitude and Reflectance Sensor (ScaLARS).", In Proceedings of the Second International Airborne Remote Sensing Conference and Exhibition, San Francisco, California, Vol. I, pp. I-429 I-438, June K. Kraus and N. Pfeifer, "Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data," ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 53, No. 4, pp , J. Lindenberger, Laser-Profilmessung zur topographischen Geländeaufnahme, Ph.D. Dissertation, Deutsche Geodätische Kommission bei der Bayerischen Akademie der Wissenschaften, Reihe C, Heft Nr. 400, pp , 1993.