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A double-strategy-check active learning algorithm for hyperspectral image classification / Ying Cui in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 11 (November 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 11 (November 2019) Titre : A double-strategy-check active learning algorithm for hyperspectral image classification Type de document : Article/Communication Auteurs : Ying Cui, Auteur ; Xiaowei Ji, Auteur ; Kai Xu, Auteur ; Liguo Wang, Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification semi-dirigée [Termes IGN] image hyperspectrale Résumé : (Auteur) Applying limited labeled samples to improve classification results is a challenge in hyperspectral images. Active Learning (AL) and Semisupervised Learning (SSL) are two promising techniques to achieve this challenge. Combining AL with SSL is an excellent idea for hyperspectral image classification. The traditional method, such as the Collaborative Active and Semisupervised Learning algorithm (CASSL), may introduce many incorrect pseudolabels and shows premature convergence. To overcome these drawbacks, a novel framework named Double-Strategy-Check Collaborative Active and Semisupervised Learning (DSC-CASSL) is proposed in this paper. This framework combines two different AL algorithms and SSL in a collaborative mode. The double-strategy verification can gradually improve the pseudolabeling accuracy and facilitate SSL. We evaluate the performance of DSC-CASSL on four hyperspectral data sets and compare it with that of four hyperspectral image classification methods. Our results suggest that DSC-CASSL leads to consistent improvement for hyperspectral image classification. Numéro de notice : A2019-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.85.11.841 Date de publication en ligne : 01/11/2019 En ligne : https://doi.org/10.14358/PERS.85.11.841 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94067 [article]

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