A COMPARISON THROUGH TREE EXTRACTION IN IMAGE-SPACE AND OBJECT-SPACE

Abstract. In various studies trees have been extracted and their conditions have been examined through different detection algorithms from two main data sources including (a) point cloud and (b) raster data. The output of tree extraction is the input of the next processing steps, and the importance of these outputs is proved more than before. Tree Extraction (TE) has many applications in biomass estimation, CHM extraction, etc. All of which require high accuracy and the correct position of the trees. therefore, in this study, a comparison between tree extraction algorithms in two common sources of data has been conducted. As for the raster data, all bands are first co-registered. Afterward, the trees are separated from the background by using image processing techniques such as changing the image color space and weighted averaging on different bands. Finally, TE algorithms such as watershed segmentation, valley following, local maxima, and image binarization were applied. As for the point cloud data, TE can be conducted in the object space to compensate for the methods used in the raster space with object detection algorithms e.g., the coherence between the two trees, etc. which have been discussed in detail in this paper. In the object space, three algorithms, region-based, surface normal, and Euclidean segmentation, were implemented and discussed on the same raster data set in the photogrammetric point cloud. The results show the higher accuracy of the region-based algorithm in object-space by more than 26% in comparison with the valley following algorithm in image space.