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Local and global evaluation for remote sensing image segmentation / Tengfei Su in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 256 - 276 Titre : Local and global evaluation for remote sensing image segmentation Type de document : Article/Communication Auteurs : Tengfei Su, Auteur ; Shengwei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 256 - 276 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] discrétisation [Termes IGN] erreur [Termes IGN] objet géographique [Termes IGN] segmentation d'image Résumé : (Auteur) In object-based image analysis, how to produce accurate segmentation is usually a very important issue that needs to be solved before image classification or target recognition. The study for segmentation evaluation method is key to solving this issue. Almost all of the existent evaluation strategies only focus on the global performance assessment. However, these methods are ineffective for the situation that two segmentation results with very similar overall performance have very different local error distributions. To overcome this problem, this paper presents an approach that can both locally and globally quantify segmentation incorrectness. In doing so, region-overlapping metrics are utilized to quantify each reference geo-object's over and under-segmentation error. These quantified error values are used to produce segmentation error maps which have effective illustrative power to delineate local segmentation error patterns. The error values for all of the reference geo-objects are aggregated through using area-weighted summation, so that global indicators can be derived. An experiment using two scenes of very different high resolution images showed that the global evaluation part of the proposed approach was almost as effective as other two global evaluation methods, and the local part was a useful complement to comparing different segmentation results. Numéro de notice : A2017-516 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.06.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86478 [article]

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