Individual Tree Detection and Crown Delineation with 3D Information from Multi-view Satellite Images
This work addresses forest inventory management by improving remote sensing surveys, though it appears incremental as it builds on existing methods with 3D data.
The paper tackled the problem of individual tree detection and crown delineation from satellite images by using 3D information from multi-view data, achieving a best overall detection accuracy of 89% in experiments.
Individual tree detection and crown delineation (ITDD) are critical in forest inventory management and remote sensing based forest surveys are largely carried out through satellite images. However, most of these surveys only use 2D spectral information which normally has not enough clues for ITDD. To fully explore the satellite images, we propose a ITDD method using the orthophoto and digital surface model (DSM) derived from the multi-view satellite data. Our algorithm utilizes the top-hat morphological operation to efficiently extract the local maxima from DSM as treetops, and then feed them to a modi-fied superpixel segmentation that combines both 2D and 3D information for tree crown delineation. In subsequent steps, our method incorporates the biological characteristics of the crowns through plant allometric equation to falsify potential outliers. Experiments against manually marked tree plots on three representative regions have demonstrated promising results - the best overall detection accuracy can be 89%.