Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks

dc.contributor.authorKhan, Salman
dc.contributor.authorHe, Xuming
dc.contributor.authorPorikli, Fatih
dc.contributor.authorBennamoun, Mohammed
dc.date.accessioned2021-06-23T23:06:25Z
dc.date.issued2017
dc.date.updated2020-11-23T10:33:51Z
dc.description.abstractLand cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions.en_AU
dc.description.sponsorshipThe authors would like to thank Geoscience Australia for providing the data and expert annotations. They would also like to thank the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this paper.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0196-2892en_AU
dc.identifier.urihttp://hdl.handle.net/1885/238217
dc.language.isoen_AUen_AU
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)en_AU
dc.rights© 2017 IEEEen_AU
dc.sourceIEEE Transactions on Geoscience and Remote Sensingen_AU
dc.source.urihttps://ieeexplore.ieee.org/document/7948741en_AU
dc.subjectChange detectionen_AU
dc.subjectdeep learningen_AU
dc.subjectimage inpaintingen_AU
dc.subjectmultitemporal spectral dataen_AU
dc.subjectremote sensingen_AU
dc.titleForest Change Detection in Incomplete Satellite Images With Deep Neural Networksen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue9en_AU
local.bibliographicCitation.lastpage5423en_AU
local.bibliographicCitation.startpage5407en_AU
local.contributor.affiliationKhan, Salman, Data61-CSIROen_AU
local.contributor.affiliationHe, Xuming, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationPorikli, Fatih, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationBennamoun, Mohammed, University of Western Australiaen_AU
local.contributor.authoremailu4981609@anu.edu.auen_AU
local.contributor.authoruidHe, Xuming, u4981609en_AU
local.contributor.authoruidPorikli, Fatih, u5405232en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor080106 - Image Processingen_AU
local.identifier.absfor080104 - Computer Visionen_AU
local.identifier.absfor170203 - Knowledge Representation and Machine Learningen_AU
local.identifier.ariespublicationa383154xPUB7410en_AU
local.identifier.citationvolume55en_AU
local.identifier.doi10.1109/TGRS.2017.2707528en_AU
local.identifier.scopusID2-s2.0-85021757762
local.identifier.thomsonID000408346600046
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://ieeexplore.ieee.orgen_AU
local.type.statusPublished Versionen_AU

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