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Segmentation of tree crown model with complex structure from airborne LIDAR data

The objective in this study is to obtain the accurate tree crown model with complex structure from airborne lidar data for latter feature extraction. The segmentation of tree crown was implemented in several phases. First, the relatively high vegetation points were filtered out from the original tree dimensional point cloud by lidar data processing software. These vegetation points were interpolated in a grid, and then lowpass filter and highpass filter method were utilized to smooth the noise and sharp the crown edge respectively. In the next phase, the points were transformed to be a grayscale image, and the contrast of the image was enhanced by a contrast stretch algorithm to help the segmentation in latter step. Before the watershed segmentation was used to segment the tree crowns, the opening and closing operation in morphology were operated on the image to optimize the segmentation. Finally, a satisfying segmentation result was shown compared to the result which the contrast stretch algorithm wasn't operated on the image, even the overlapped tree crowns were segmented successfully in our test.