The Study of Improved Marker-Controlled Watershed Crown Segmentation Algorithm

Automated or semi automated tree detection and crown delineation using high spatial resolution remotely sensed imagery provides a potentially efficient means to acquire information needed for forest management decisions, sustainable forest management. The presented approach develops an improved mathematical morphology based marker-controlled watershed crown segmentation algorithm for crown segmentation. This method is be put on the Quick Bird satellite images in Populus I-72 plantation even stand at Nan Gen village Hai Kou town in Anhui Province of China. Segmentation using the watershed transform works better if you can identify or mark foreground objects and background locations. We analyze the theoretic model, applicability, precision, experiment condition, verification method, error analyse and limitation of this method. This algorithm does not take into account the classification and only gets the image segment for further analyzing. We overlap the segmentation result with original image by manually crown delineation. By visual appraise, this algorithm works well. Average tree numbers identification error is 36%. We discuss the improvement ways to get better results.