PalenichkaDoyonLakhssassiEtAl2012

Reference

Palenichka, R., Doyon, F., Lakhssassi, A., Zaremba, M. (2012) Hierarchical multi-scale segmentation of LiDAR images in forest areas. In International Geoscience and Remote Sensing Symposium (IGARSS). Pages 5462-5465. (Scopus )

Abstract

A two-level hierarchical method for LiDAR image segmentation in forest areas is proposed. This method represents a multi-scale analysis of LiDAR images by an attention operator at different scale ranges and for all pixels to detect feature points. As a result, the feature points as optimal seed locations for regiong-rowing segmentation are extracted and scale-adaptive region growing is applied at the seeds. At the second level, the final segmentation by the scale-adaptive region growing provides individual tree crowns. The conducted experiments confirmed the reliability of the proposed segmentation method and have shown its high potential in LiDAR image analysis for object detection. © 2012 IEEE.

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@INPROCEEDINGS { PalenichkaDoyonLakhssassiEtAl2012,
    AUTHOR = { Palenichka, R. and Doyon, F. and Lakhssassi, A. and Zaremba, M. },
    TITLE = { Hierarchical multi-scale segmentation of LiDAR images in forest areas },
    YEAR = { 2012 },
    PAGES = { 5462-5465 },
    NOTE = { cited By 2 },
    ABSTRACT = { A two-level hierarchical method for LiDAR image segmentation in forest areas is proposed. This method represents a multi-scale analysis of LiDAR images by an attention operator at different scale ranges and for all pixels to detect feature points. As a result, the feature points as optimal seed locations for regiong-rowing segmentation are extracted and scale-adaptive region growing is applied at the seeds. At the second level, the final segmentation by the scale-adaptive region growing provides individual tree crowns. The conducted experiments confirmed the reliability of the proposed segmentation method and have shown its high potential in LiDAR image analysis for object detection. © 2012 IEEE. },
    ART_NUMBER = { 6352370 },
    AUTHOR_KEYWORDS = { attention operator; LiDAR image; local scale; region growing; segmentation; tree crown detection },
    DOCUMENT_TYPE = { Conference Paper },
    DOI = { 10.1109/IGARSS.2012.6352370 },
    JOURNAL = { International Geoscience and Remote Sensing Symposium (IGARSS) },
    KEYWORDS = { attention operator; Different scale; Forest area; Hierarchical method; High potential; Individual tree crown; Local scale; Multi scale analysis; Multiscale segmentation; Object Detection; Region growing; Second level; Segmentation methods; Tree crowns, Forestry; Geology; Optical radar; Remote sensing, Image segmentation, Forestry; Geology; Image Analysis; Radar; Remote Sensing; Segmentation },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873140596&partnerID=40&md5=c5e60815d2027156fcf84815beaaa95f },
}

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