A novel active learning technique for multi-label remote sensing image scene classification

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dc.contributor.authorTeshome Zegeye, Bayable
dc.contributor.authorDemir, Begüm
dc.date.accessioned2019-11-25T19:19:42Z
dc.date.available2019-11-25T19:19:42Z
dc.date.issued2018-10-09
dc.descriptionCopyright 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.en
dc.description.abstractThis paper presents a novel multi-label active learning (MLAL) technique in the framework of multi-label remote sensing (RS) image scene classification problems. The proposed MLAL technique is developed in the framework of the multi-label SVM classifier (ML-SVM). Unlike the standard AL methods, the proposed MLAL technique redefines active learning by evaluating the informativeness of each image based on its multiple land-cover classes. Accordingly, the proposed MLAL technique is based on the joint evaluation of two criteria for the selection of the most informative images: i) multi-label uncertainty and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the multi-label classification algorithm in correctly assigning multi-labels to each image, whereas multi-label diversity criterion aims at selecting a set of un-annotated images that are as more diverse as possible to reduce the redundancy among them. In order to evaluate the multi-label uncertainty of each image, we propose a novel multi-label margin sampling strategy that: 1) considers the functional distances of each image to all ML-SVM hyperplanes; and then 2) estimates the occurrence on how many times each image falls inside the margins of ML-SVMs. If the occurrence is small, the classifiers are confident to correctly classify the considered image, and vice versa. In order to evaluate the multi-label diversity of each image, we propose a novel clustering-based strategy that clusters all the images inside the margins of the ML-SVMs and avoids selecting the uncertain images from the same clusters. The joint use of the two criteria allows one to enrich the training set of images with multi-labels. Experimental results obtained on a benchmark archive with 2100 images with their multi-labels show the effectiveness of the proposed MLAL method compared to the standard AL methods that neglect the evaluation of the uncertainty and diversity on multi-labels.en
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarthen
dc.identifier.eissn1996-756X
dc.identifier.isbn9781510621626
dc.identifier.isbn9781510621619
dc.identifier.issn0277-786X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10382
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9342
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otheractive learningen
dc.subject.othermulti-label uncertaintyen
dc.subject.othermulti-label diversityen
dc.subject.othermulti-label classificationen
dc.subject.otherremote sensingen
dc.titleA novel active learning technique for multi-label remote sensing image scene classificationen
dc.typeConference Objecten
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber107890Ben
dcterms.bibliographicCitation.doi10.1117/12.2500191en
dcterms.bibliographicCitation.editorBruzzone, Lorenzo
dcterms.bibliographicCitation.editorBovolo, Francesca
dcterms.bibliographicCitation.originalpublishernameSPIEen
dcterms.bibliographicCitation.originalpublisherplaceBellingham, Wash.en
dcterms.bibliographicCitation.proceedingstitleProceedings of SPIE 10789 – Image and Signal Processing for Remote Sensing XXIVen
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Groupde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Remote Sensing Image Analysis Groupde
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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