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Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 174 - 189 Titre : Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques Type de document : Article/Communication Auteurs : Chinsu Lin, Auteur ; Shih-Yu Chen, Auteur ; Chia-Chun Chen, Auteur ; Chia-Huei Tai, Auteur Année de publication : 2018 Article en page(s) : pp 174 - 189 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse d'image orientée objet [Termes IGN] changement climatique [Termes IGN] croissance des arbres [Termes IGN] drone [Termes IGN] feuille (végétation) [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] image RVB [Termes IGN] indice de végétation [Termes IGN] Kappa de Cohen [Termes IGN] Taïwan [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Phenological events of tree leaves from initiation to senescence is generally influenced by temperature and water availability. Detection of newly grown leaves (NGL) is useful in the diagnosis of growth of trees, tree stress and even climatic change. Utilizing very high resolution UAV images, this paper examines the feasibility of NGL detection using hyperspectral detection algorithms and anomaly detectors. The issues of pixel resolution and hard decision thresholding in deriving accurate NGL maps are also explored. Results showed that the blind-detection algorithms RXDs are not suitable for NGL detection due to the spectra similarity between NGL and both mature leaves and grass, while brighter pixels, such as those produced by soil and concrete materials, are more easily recognized as anomaly in contrast to forest. Matching filter (MF) based detectors are, however, able to accurately detect NGL over forest stands and are even more effective in the sense of achieving satisfactory true positives and true negatives while providing minimal false alarms. Of the tested partial knowledge MF algorithms, the covariance matched filter based distance (KMFD) detector performed very well with overall accuracy (OA) 0.97 and kappa coefficient () 0.60 on a natural resolution of 6.75 cm image. When a variety of mature-leaf nonobjective targets are included in the detection, the orthogonal subspace projector (OSP) tends to suppress NGL pixels as an unwanted signature and this leads to poor detection. Conversely, the target constrained interference minimized filter (TCIMF) detector is still able to effectively detect NGL with a satisfactory OA and through effective matching filter of the target signature as the hard-decision threshold is subject to a level of 5% or 1% probability of false alarms. From decimeter resolution satellite images, the KMFD and TCIMF detectors are capable of achieving an accuracy of OA = 0.94 and  = 0.56 or OA = 0.87 and  = 0.48 for images with a resolution of 33.75 cm or 67.50 cm respectively. This indicates that hyperspectral target detection techniques have great potential in NGL detection via high spatial resolution satellite multispectral images. Numéro de notice : A2018-294 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.022 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90412 [article]

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