Tree detection from aerial imagery
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Contribution of airborne full-waveform lidar and image data for urban scene classification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
2D tree detection in large urban landscapes using aerial LiDAR data
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Classifying urban landscape in aerial LiDAR using 3D shape analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this work, we classify 3D aerial LiDAR height data into roads, grass, buildings, and trees using a supervised parametric classification algorithm. Since the terrain is highly undulating, we subtract the terrain elevations using digital elevation models (DEMs, easily available from the United States Geological Survey (USGS)) to obtain the height of objects from a flat level. In addition to this height information, we use height texture (variation in height), intensity (amplitude of lidar response), and multiple (two) returns from lidar to classify the data. Furthermore, we have used luminance (measured in the visible spectrum) from aerial imagery as the fifth feature for classification. We have used mixture of Gaussian models for modeling the training data. Model parameters and the posterior probabilities are estimated using Expectation-Maximization (EM) algorithm. We have experimented with different number of components per model and found that four components per model yield satisfactory results. We have tested the results using leave-one-out as well as random \frac{n}{2} test. Classification results are in the range of 66%-84% depending upon the combination of features used that compares very favorably with. train-all-test-all results of 85%. Further improvement is achieved using spatial coherence.