Tree crown recognition algorithm on high spatial resolution remote sensing imagery

To extract information at the individual tree level, which is very useful in biology, ecology and forestry, would be prohibitively time-consuming and be necessary for artificial intelligence by considering many factors. The presented approach develops a tree top seeded based region growth tree detection and crown delineation algorithm for analyzing QuickBird satellite images in Populus × xiaohei plantation even stand at Xue Jia Zhuang wood farm in Shanxi Province of China. After multi resolution segmentation, we get image object segments for tree top seeds detection with NDVI and ratio NIR feature. Around theses seeds, we let them region growing in a cycle way. Some false seeds must be wiped off with given feature threshold. After quad tree segmentation for crown shape optimization, the same category region must be merged. We use 9 plots with different plantation density to validate the above method. Average tree numbers identification error is 18.9%, R² = 0.4693. From comparing tree numbers of field work and software identification by tree matching, the confusion matrix, overall accuracy, commission error, omission error is computed.