Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Using Three-Dimensional Features to Improve Terrain Classification
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Error Detection and DEM Fusion Using Self-Consistency
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
| Hi-index | 0.00 |
This paper describes an ongoing collaborative research program between the Computer Science and the Forestry and Wildlife Management Departments at the University of Massachusetts to develop cost-effective methodologies for monitoring biomass and other environmental parameters over large areas. The data acquisition system consists of a differential GPS system, a 3-axis solid state inertial reference system, a small format (70mm) aerial survey camera, two video cameras, a laser profiling altimeter, and a PC based data recording system. Two aerial survey techniques for determining biomass are discussed. One primarily based on video and the other relying additionally on the 3D terrain models generated from the aerial photographs. In the first technique, transects are flown at 1,000 feet with dual-camera wide angle and zoom video, and a profiling laser operating at 238 Hz. The video coverage is used to identify individual tree species, and the laser profiler is used to estimate tree heights. The second procedure builds on this approach by taking sequences of 70mm photographs with an 80% overlap along a second higher altitude flight line at 4,000 feet. Detailed 3D terrain models are then generated from successive pairs of images. Several state-of-the-are computer vision algorithms are discussed, including the ITL system, which is an interactive ground cover classification system that allows an operator to quickly classify the large areas in a real-time, and Terrest, which is a highly robust 3D terrain modeling system. The work described in this paper is in a preliminary phase and all of the constituent technologies have not been fully integrated, we nevertheless demonstrated the value and feasibility of using computer vision techniques to solve environmental monitoring problems on a large scale.