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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorFung, CH-
dc.creatorWong, MS-
dc.creatorChan, PW-
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
dc.rightsThe following publication Fung, C.H.; Wong, M.S.; Chan, P.W. Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network. Remote Sens. 2019, 11, 2077, 1-21 is available at
dc.subjectSpatio-temporal data fusionen_US
dc.subjectHopfield neural networken_US
dc.subjectSatellite imagesen_US
dc.titleSpatio-temporal data fusion for satellite images using hopfield neural networken_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractSpatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, Sept. 2019, v. 11, no. 18, 2077, p. 1-21-
dcterms.isPartOfRemote sensing-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
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