Georg S. Ruppert

Assessment of forest attributes and single-tree segmentation by means of laser scanning

In this study laser scanner canopy height metrics data from the laser scanner Toposys-1 were investigated to derive forest attributes such as timber volume, tree height, and crown area coverage for the use in forest inventories. Investigations were based both on single tree information from crown segmentation and stand-wise assessments. while the statistical stand-wise approach only utilizes mean values for stand areas, the single tree classification approach makes use of the full potential of the high resolution laser scanner data. Forest inventory parameters were classified on the base of single trees or small groups of trees using digital image processing methods such as segmentation and data filtering. Stand-wise forest inventory data and single tree information were regressed against laser-derived features. Accuracy for additional stand parameters depends on crown closure and tree species. The obtained accuracy for tree heights from the approaches described is within the accuracy of conventional field based measurements. Further, it was investigated in how far laser scanner data is appropriate to assess timber volume. The described approaches can be used operationally for stand- wise forest inventories. Especially the single tree approach can be used instead of time- and cost-intensive field work in cases when full enumeration is required.

Accuracy of laser scanning for DTM generation in forested areas

This paper evaluates and discuses the accuracy of laser scanner in DTM (digital terrain model) generation in forested and suburban areas. Special emphasis is laid in order to optimize the selection of ground hits used for the creation of the DTM of future high-pulse-rate laser scanners. A novel DTM algorithm is depicted in detail. The algorithm is based on five phases: (1) calculation of the original reference surface, (2) classification of vegetation and removal of the vegetation from the reference surface, (3) classification of the original cloud of points using the reference surface, (4) calculation of the DTM based on the classified ground hits, and (5) interpolation of the missing points. Standard error of 15 cm was obtained for flat forest areas and the error increased with increasing terrain slope to the value of approximately 40 cm at the slope of 40%. The average standard error for forest area was slightly better than 25 cm. The laser-derived DTM of the forest road deviated only 8.5 cm from the true height. An optimum performance for the DTM generation was obtained by averaging the ground hits which located, at the maximum, 60 cm above the minimum terrain values. A simplified algorithm was suggested for more operational use based on the first pulse mode data. Special cases of the suburban area DTM were verified including terrain heights below the buildings and bridges, terrain heights of roads, terrain heights below large outdoor light fixture, to name but a few. About 100 special cases in suburban/urban environment for DTM verification were searched. The corresponding standard error between the laser-derived values and reference data was 45 cm.

Adaptive multiresolutional algorithm for high-precision forest floor DTM generation

This paper focuses on an adaptive multi-resolutional algorithm for generating forest floor digital elevation models by processing the three dimensional data acquired by the laser scanner. The adaptivity of our algorithm ensures that it can be used successfully in flat, hilly, and mountainous terrain and deliver accurate results. A large set of GPS ground reference points are used to verify the algorithm along with others commonly used. First results show that the average error is between 0,5 and 1m for an Alpine region in Austria which is very close to the error the laser scanner data distributor claims for flat terrain. This study is part of the HIGH-SCAN project (EU IV Framework/Center of Earth Observation), a project whose objective is to provide methods to integrate high satellite imagery and laser scanner data for forest inventory.

FFT-descriptors for shape recognition of military vehicles

An accurate method to detect and classify military vehicles based on the recognition of shapes is presented in this work. FFT-Descriptors are used to generate a scale, translation and rotation invariant characterization of the shape of such an object. By interpreting the boundary pixels of an object as complex numbers it is possible to calculate an FFT-Descriptor based on the spectrum of a Fast Fourier Transform of these numbers. It is shown that by using this characterization it is possible to match such representations with models in a database of known vehicles and thereby gaining a highly robust and fault tolerant object classification. By selecting a specific number of components of a FFT-Descriptor the classification process can by tailored to different needs of recognition accuracy, allowed shape deviation and classification speed.

A hybrid classifier for remote sensing applications.

This paper presents a hybrid-unsupervised and supervised-classifier for land use classification of remote sensing images. The entire satellite image is quantized by an unsupervised Neural Gas process and the resulting codebook is labeled by a supervised majority voting process using the ground truth. The performance of the classifier is similar to that of Maximum Likelihood and is only a little worse than Multilayer Perceptions while training and classifying requires no expert knowledge after collecting the ground truth. The hybrid classifier is much better suited to classifications with complex non-normally distributed classes than Maximum Likelihood. The main advantage of the Neural Gas classifier, however, is that it requires much less user interaction than other classifiers, especially Maximum Likelihood.

Robust measure for camouflage effectiveness in the visual domain

A human-in-the-loop computer based camouflage assessment approach was already presented at the AeroSense 1998 conference.3 The same image sets were used for human photosimulation as well as for the computer assessment method. The human photosimulation results suggested four camouflage classes which were used to develop and verify the separability measure. Analyzing camouflage effectiveness using separability measures induces a very complex feature space. Best results were obtained using the C4.5 classifier as a separability measure. The size of the objects presented duing the photosimulation sessions and tactical knowledge of the observers had significant influence on the detectoin/recognition performance of humans. The most important advantage of our method is to make camouflage assessment more transparent and deterministic. Results of a selected experiment during a field test are shown in this paper.

Fuzzy logic approach for the quantitative assessment of camouflage effectiveness in the thermal infrared domain

A key point for good camouflage results in the thermal infrared domain lies in the ability of the camouflage system to adapt to the thermal emission behavior of the surrounding background. In order to obtain reliable assessments of the camouflage effectiveness, evaluation has to take place under various environment condition. The combination of the different results leads to a assessment measure with the demanded reliability. The object quantization of the individual camouflage effectiveness and the following combination is very difficult to achieve by human operators. Therefore an Infrared Camouflage Effectiveness Assessment Tool (ICEAT) has been developed, which needs only minor human interaction and supports the automated combination of the results of various test scenes. In a first step hot spots of the object and the background are detected. In a second phase various features are calculated which are combined to a single assessment measure in the third phase by using fuzzy logic. The fuzzy logic approach has the advantage that the customization of the ICEAT can be achieved by simply modifying the used membership functions.

Evaluation of laser scanner data in forest areas

The laser data acquired by the airborne laser scanner, called TopoSys-1, in the forest area has been evaluated in this study. The test site was the boreal forest of 0.5 km by 2.0 km in size in southern Finland. The laser scanning system provided 127 measurements per scan line across the flight direction at the pulse repetition rate of 83 kHz. Both the First pulse and Last pulse modes were employed. The position of the carrying platform was determined using the integrated DGPS/INS system. The original laser data set used in the analyses was geocoded but unfiltered. The files only consisted of X, Y, Z coordinates of each sample. First, the whole laser data were classified in four classes, namely: ground, vegetation, buildings, and erroneous points using the TerraScan software on top Microstation SE. After that the ground points were further processed when the still present vegetation removed. Finally, the Digital Elevation Models (DEMs) were generated. The terrain variation was from 91.3 m to 128.4 m. The DEM made from the laser data was compared with the reference DEM available at that moment. The systematic error was 1.03 meters. The standard deviation between the two DEMs was 2.77 meters. Using ground control points, the systematic error can be overcome. However, a more accurate reference DEM is needed for the future comparison. In addition, a map of the true tree heights in the area was generated at the cell size of 1 m by 1 m. The clear-cut areas can be more easily seen than using the aerial photographs.