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Multi-Scale Segmentation of Forest Areas and Tree Detection in LiDAR Images by the Attentive Vision Method

A scale-adaptive method for object detection and LiDAR image segmentation in forest areas using the attentive vision approach to remote sensing image analysis is proposed. It provides an effective solution to the general task of object segmentation defined as the subdivision of image plan into multiple objects regions against the background region. This method represents a multi-scale analysis of LiDAR images by an attention operator at different scale ranges and for all pixel locations to detect feature points. Besides the initial height image, the operator also uses primitive feature maps (components) to reliably detect objects of interest such as individual trees or entire forest stands. As a result, feature points representing the optimal seed locations for region-growing segmentation are extracted and scale-adaptive region growing is applied at the seed locations. At the second level, the final segmentation by the scale-adaptive region growing provides delineation of individual tree crowns. The conducted experiments confirmed the reliability of the proposed method and showed its high potential in LiDAR image analysis for object detection and segmentation.