SUPERVISED CLASSIFICATION USING AERIAL PHOTOGRAPHS AND LIDAR DATA

Digital cameras are becoming common for aerial photography. They have better radiometric and spectral resolution than film cameras. Besides, their ground resolution and geometric accuracy are good enough for most applications. LIDAR instruments have been developed rapidly over past years. Surface model from the object can be determined exactly using LIDAR data and vegetation coverage or weather conditions are not generally a problem. There are a lot of applications where aerial imagery and LIDAR data are used together and in future they are probably going to fuse into one instrument. Several advantages in object interpretation are gained when LIDAR data is combined with aerial imagery because both reflection properties and geometric information are observed. The purpose of this work is to classify test area and improve classification results using both LIDAR data and digital aerial photographs. The goal is to find the best working feature sets including spectral and geometric information. Finnish Geodetic Institute enforced aerial measurements at Espoo in southern Finland using air-borne laser scanner and digital aerial camera. Small test area contains mainly urban area and mixed forests. Laser scanning was made using Optech ALTM 2033 instruments in June 2004. One flight line was flown several times and the point density was over 10 points per square meter. Aerial images were taken using Vexcel Ultracam-D instrument which is capable of detect visual and infrared radiation. The flying height was 600 m and pixel size was about 20 cm. Supervised classification was performed using general Bayesian learning. Training data was gath-ered using field measurements. Before classification, separability measures between each training class were calculated using different feature sets that included spectral and geometric features, and finally the data was classified using approved features and Bayesian classification. Separabil-ity measures calculated from the training data using spectral and geometric features showed that geometric information of LIDAR data improved slightly class separability. The results after Bayes-ian classification using different feature sets were similar. Spectral and geometric features to-gether led to the best results. INTRODUCTION Remote sensing has been recognized as an efficient tool for forest inventory (1). It allows us to observe accurate and up-to-date data from large forest areas. Airborne laser scanners provide information about forest geometry and digital aerial cameras provide information about forest radiometry. Usually, different wood species can be distinguished using several bands in the visual and near infrared part of the electromagnetic spectrum. This is based on the fact that different species have characteristic spectral reflectance. However, the reflectance values of two different plants are sometimes very similar. This leads to the problem that certain vegetation classes mix together. Proceedings of the 2 Workshop of the EARSeL SIG on Land Use and Land Cover 288 Different wood species have different structure and geometry. Plants with similar spectral reflectance may have different structure and geometric information of LIDAR instruments can be used to improve classification results. In this study, laser scanning was made using Optech ALTM 2033 instruments in June 2004. One flight line was flown several times and the point density was over 10 points per square meter. Aerial images were taken using Vexcel Ultracam-D instrument which is capable of detect visual and infrared radiation. The flying height was 600 m and pixel size was about 20 cm. Optech ALTM 2033 laser scanner data and aerial images of Vexcel Ultracam-D were investigated in this study and the main purpose was to combine two different data sets and perform supervised classification using both spectral and geometric data. METHODS Test area Aerial measurements with laser scanner and aerial camera were made at Espoonlahti in Southern Finland ordered by Finnish Geodetic Institute. Small test area contains mainly urban area and mixed forests. Laser scanning was made using Optech ALTM 2033 instruments in June 2004. One flight line was flown several times, and so the point density was over 10 points per square meter. Aerial images were taken using Vexcel Ultracam-D instrument which is capable of detect visual and infrared radiation. The flying height was 600 m and pixel size was about 20 cm. Table 1: Parameters of laser scanner data and aerial images. Figure 1: Figure 2: A part of the aerial photo (left) and LIDAR height (above) and intensity (below) data from first (left) and last (right) pulses. Digital aerial images Laser scanner data Camera Vexcel Ultracam-D Instrument Optech ALTM 2033 Objective f = 101.4 mm Flight altitude 600 m Aperture 5.6 Pulse frequency 33 000 Hz Exposure time 1/125 – 1/175 s Field of View ± 9 degrees Flying height 600 m Measurement density 2 per m2 GSD ~20 cm Swath width ~185 m Center for Remote Sensing of Land Surfaces, Bonn, 28-30 September 2006 289 Field surveys Terrestrial field photographs and other field training data were obtained during the summer of 2005 and they were used as a reference data for classification. Training area inventory included different man-made features and wood species. For example, Norway spruce, Scotch pine and birch. Figure 2: Field photographs from the study area. Preliminary works Geometric correction of aerial images was made using PCI Geomatica software. Ground control points were measured earlier by Finnish Geodetic Institute. Laser scanner points were converted to raster data and several different laser feature images were extracted. Laser feature images and original aerial images were examined that image features matched together. Figure 3 shows several feature images: 1. Represents LIDAR point average calculated from 1. pulse height data 2. Represents LIDAR point average calculated from last pulse height data 3. Represents LIDAR point standard deviation calculated from 1. pulse height data 4. Represents LIDAR point standard deviation calculated from last pulse height data 5. Represents LIDAR point average calculated from 1. pulse intensity data Figure 3: Laser feature images that were extracted from the LIDAR data. Proceedings of the 2 Workshop of the EARSeL SIG on Land Use and Land Cover 290 6. Represents LIDAR point average calculated from last pulse intensity data 7. Represents LIDAR point standard deviation calculated from 1. pulse intensity data 8. Represents LIDAR point standard deviation calculated from last pulse intensity data Figure 4: RGB composite images of extracted LIDAR images. RESULTS Separability measures Forest classes in this study were Norway spruce, Scotch pine, aspen, alder and birch. Separability measures between each class were calculated using the Bhattacharya Distance (2). Table 2 shows average separabilities calculated using 4 aerial image channels and 8 feature images extracted from LIDAR data. Separability measure results were slightly better when using LIDAR feature images together with aerial images. Generally, there were problems with deciduous trees. Their reflectance and geometry were similar and different species mixed together. Image classification Image classification was made using Bayesian algorithm. The area was classified into the following different classes: road, dirt road, building, grass, sand, Norway spruce, Scotch pine and birch. The classification was performed using two different feature sets. The first classification was performed using 4 aerial image channels and the second classification was performed using 4 aerial image channels and 8 LIDAR feature images. Table 2: Average separabilities of forest classes: 0 < x < 1 (very poor separability); 1 < x < 1.9 (poor separability); 1.9 < x < 2 (good separability). Features Average separability 4 aerial image channels 1.42 4 aerial image channels + 8 LIDAR features 1.68 Center for Remote Sensing of Land Surfaces, Bonn, 28-30 September 2006 291 Figure 5: Classification result of the Bayesian algorithm and average and overall accuracies. The average accuracy is the average of the accuracies for each class. Overall accuracy is a similar average with the accuracy weighted by the proportion of test samples. Average and overall accuracies were calculated when data was classified using 4 aerial image channels and 8 LIDAR feature images. Average and overall accuracy was about 10 % better when both spectral and geometric features were used. CONCLUSIONS Separability measures proved that different wood species are easier to distinguish when both aerial images and LIDAR data were used together. Results after Bayesian classification using different feature sets were similar. Spectral and geometric features together led to the best results. Average and overall accuracies improved about 10 %.