In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.
In this paper, a method for individual tree shape modeling and canopy coverage delineation is provided for high density airborne LiDAR data. Three basic 3-D canopy shape models are introduced as fundamental assumptions, and then an iterative algorithm for calculating tree canopy window is implemented. After that, the prototype test is carried out with simulated forest point data which visually shows a valid result. After that, a real mixed forest LiDAR dataset is being put into experiment. Based on the same theory, the output and a statistical analysis reveals that the proposed method can yield an effective and distinguishable extraction of different tree canopy coverage delineation.