Approximating JPEG 2000 wavelet representation through deep neural networks for remote sensing image scene classification

dc.contributor.authorPreethy Byju, Akshara
dc.contributor.authorSumbul, Gencer
dc.contributor.authorDemir, Begüm
dc.contributor.authorBruzzone, Lorenzo
dc.date.accessioned2019-11-25T19:28:27Z
dc.date.available2019-11-25T19:28:27Z
dc.date.issued2019-10-15
dc.descriptionCopyright 2019 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 approach based on the direct use of deep neural networks to approximate wavelet sub-bands for remote sensing (RS) image scene classification in the JPEG 2000 compressed domain. The proposed approach consists of two main steps. The first step aims to approximate the finer level wavelet sub-bands. To this end, we introduce a novel Deep Neural Network approach that utilizes the coarser level binary decoded wavelet sub-bands to approximate the finer level wavelet sub-bands (the image itself) through a series of deconvolutional layers. The second step aims to describe the high-level semantic content of the approximated wavelet sub- bands and to perform scene classification based on the learnt descriptors. This is achieved by: i) a series of convolutional layers for the extraction of descriptors which models the approximated sub-bands; and ii) fully connected layers for the RS image scene classification. Then, we introduce a loss function that allows to learn the parameters of both steps in an end-to-end trainable and unified neural network. The proposed approach requires only the coarser level wavelet sub-bands as input and thus minimizes the amount of decompression applied to the compressed RS images. Experimental results show the effectiveness of the proposed approach in terms of classification accuracy and reduced computational time when compared to the conventional use of Convolutional Neural Networks within the JPEG 2000 compressed domain.en
dc.identifier.eissn1996-756X
dc.identifier.isbn9781510630147
dc.identifier.isbn9781510630130
dc.identifier.issn0277-786X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10383
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9343
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otherscene classification in compressed domainen
dc.subject.otherJPEG 2000en
dc.subject.otherdeep neural networken
dc.subject.otherremote sensingen
dc.titleApproximating JPEG 2000 wavelet representation through deep neural networks for remote sensing image scene classificationen
dc.typeConference Objecten
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber111550Sen
dcterms.bibliographicCitation.doi10.1117/12.2534643en
dcterms.bibliographicCitation.editorBruzzone, Lorenzo
dcterms.bibliographicCitation.editorBovolo, Francesca
dcterms.bibliographicCitation.originalpublishernameSPIEen
dcterms.bibliographicCitation.originalpublisherplaceBellingham, Wash.en
dcterms.bibliographicCitation.proceedingstitleProceedings of SPIE 11155 – Image and Signal Processing for Remote Sensing XXVen
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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