PalenichkaDoyonLakhssassiEtAl2013

Reference

Palenichka, R., Doyon, F., Lakhssassi, A., Zaremba, M.B. (2013) Multi-scale segmentation of forest areas and tree detection in lidar images by the attentive vision method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3):1313-1323. (Scopus )

Abstract

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. © 2013 IEEE.

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@ARTICLE { PalenichkaDoyonLakhssassiEtAl2013,
    AUTHOR = { Palenichka, R. and Doyon, F. and Lakhssassi, A. and Zaremba, M.B. },
    TITLE = { Multi-scale segmentation of forest areas and tree detection in lidar images by the attentive vision method },
    JOURNAL = { IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing },
    YEAR = { 2013 },
    VOLUME = { 6 },
    NUMBER = { 3 },
    PAGES = { 1313-1323 },
    NOTE = { cited By 4 },
    ABSTRACT = { 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. © 2013 IEEE. },
    ART_NUMBER = { 6492125 },
    AUTHOR_KEYWORDS = { Attention operator; crown detection; feature point; forest monitoring; forest structure; image segmentation; LiDAR image },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1109/JSTARS.2013.2250922 },
    KEYWORDS = { Attention operator; feature point; Forest monitoring; Forest structure; Individual tree crown; Multi scale analysis; Multiscale segmentation; Remote sensing images, Forestry; Image analysis; Image reconstruction; Object recognition; Optical radar, Image segmentation, canopy architecture; detection method; forest cover; image analysis; lidar; satellite data; satellite imagery; segmentation; vegetation cover; vegetation mapping, Forestry; Image Analysis; Monitoring; Radar },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880304939&partnerID=40&md5=a6c4fe200a07961ab15ebfc80edd560e },
}

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