{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:32:15Z","timestamp":1760239935029,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,26]],"date-time":"2019-01-26T00:00:00Z","timestamp":1548460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801211","61871218","61701272","61675051"],"award-info":[{"award-number":["61801211","61871218","61701272","61675051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central University","award":["3082017NP2017421"],"award-info":[{"award-number":["3082017NP2017421"]}]},{"DOI":"10.13039\/501100010012","name":"National Aerospace Science Foundation of China","doi-asserted-by":"publisher","award":["20185152"],"award-info":[{"award-number":["20185152"]}],"id":[{"id":"10.13039\/501100010012","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution of the original remote sensing image is coarse, and the existing SRMSAM cannot take full advantage of the spatial\u2013spectral information from the original image. To utilize more spatial\u2013spectral information, improving remote sensing image super-resolution mapping based on the spatial attraction model by utilizing the pansharpening technique (SRMSAM-PAN) is proposed. In SRMSAM-PAN, a novel processing path, named the pansharpening path, is added to the existing SRMSAM. The original coarse remote sensing image is first fused with the high-resolution panchromatic image from the same area by the pansharpening technique in the novel pansharpening path, and the improved image is unmixed to obtain the novel fine-fraction images. The novel fine-fraction images from the pansharpening path and the existing fine-fraction images from the existing path are then integrated to produce finer-fraction images with more spatial\u2013spectral information. Finally, the values predicted from the finer-fraction images are utilized to allocate class labels to all subpixels, to achieve the final mapping result. Experimental results show that the proposed SRMSAM-PAN can obtain a higher mapping accuracy than the existing SRMSAM methods.<\/jats:p>","DOI":"10.3390\/rs11030247","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T03:40:55Z","timestamp":1548733255000},"page":"247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3825-6365","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"},{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]},{"given":"Gong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]},{"given":"Siyuan","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/014311697219015","article-title":"The pixel: A snare and a delusion","volume":"18","author":"Fisher","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3558","DOI":"10.1109\/TGRS.2012.2225841","article-title":"Geometric method of fully constrained least squares linear spectral mixture analysis","volume":"51","author":"Wang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","unstructured":"Atkinson, P.M. (1997). Mapping sub-pixel boundaries from remotely sensed images. Innovations in GIS, Taylor & Francis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1109\/TGRS.2017.2748701","article-title":"Multi-objective subpixel land-cover mapping","volume":"56","author":"Ma","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"839","DOI":"10.14358\/PERS.71.7.839","article-title":"Sub-pixel target mapping from soft-classified remotely sensed imagery","volume":"71","author":"Atkinson","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/36.917895","article-title":"Super-resolution target identification form remotely sensed images using a Hopfield neural network","volume":"39","author":"Tatem","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1109\/JSTARS.2012.2191145","article-title":"Impact of land cover patch size on the accuracy of patch area representation in HNN-based super resolution mapping","volume":"5","author":"Muad","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/JSTARS.2010.2062173","article-title":"Sub-pixel mapping of tree canopy, impervious surfaces, and cropland in the Laurentian great lakes basin using MODIS time-series data","volume":"4","author":"Shao","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7203","DOI":"10.1080\/01431161.2010.519740","article-title":"Possibilities and limitations of artificial neural networks for subpixel mapping of land cover","volume":"32","author":"Nigussie","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TGRS.2017.2747624","article-title":"Object-based superresolution land-cover mapping from remotely sensed imagery","volume":"56","author":"Chen","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhou, Y., Ge, Y., An, R., and Chen, Y. (2018). Enhancing land cover mapping through integration of pixel-based and object-based classifications from remotely sensed imagery. Remote Sens., 10.","DOI":"10.3390\/rs10010077"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7747","DOI":"10.1080\/01431161.2012.702234","article-title":"A super-resolution mapping method using local indicator variograms","volume":"33","author":"Jin","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TGRS.2014.2321834","article-title":"Indicator cokriging-based subpixel mapping without prior spatial structure information","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2017.03.002","article-title":"The effect of the point spread function on sub-pixel mapping","volume":"193","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ge, Y., An, R., and Chen, Y. (2018). Super-resolution mapping of impervious surfaces from remotely sensed imagery with points-of-interest. Remote Sens., 10.","DOI":"10.3390\/rs10020242"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/2150704X.2018.1532126","article-title":"Superresolution mapping based on hybrid interpolation by parallel paths","volume":"10","author":"Wang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5397","DOI":"10.1109\/TGRS.2016.2562178","article-title":"Spatiotemporal subpixel mapping of time-series images","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1109\/LGRS.2016.2614810","article-title":"Soft-then-hard subpixel land cover mapping based on spatial-spectral interpolation","volume":"13","author":"Wang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/TGRS.2013.2267802","article-title":"Allocating classes for soft-then-hard sub-pixel mapping algorithms in units of class","volume":"5","author":"Wang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"798","article-title":"Superresolution reconstruction of multispectral data for improved image classification","volume":"5","author":"Li","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/JSTARS.2016.2552224","article-title":"Producing subpixel resolution thematic map from coarse imagery: MAP algorithm-based super-resolution recovery","volume":"9","author":"Wang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, P., Wang, L., Wu, Y., and Leung, H. (2018). Utilizing pansharpening technique to produce sub-pixel resolution thematic map from coarse remote sensing image. Remote Sens., 10.","DOI":"10.3390\/rs10060884"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1080\/01431161.2012.690541","article-title":"Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery","volume":"33","author":"Wang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, G., and Leung, H. (2018). Improving super-resolution flood inundation mapping for multispectral remote sensing image by supplying more spectral information. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2018.2882516"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.1109\/JSTARS.2015.2480120","article-title":"Super-resolution land cover mapping using multiscale self-similarity redundancy","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4480","DOI":"10.1109\/JSTARS.2015.2496660","article-title":"A new genetic method for subpixel mapping using hyperspectral images","volume":"9","author":"Tong","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.02.012","article-title":"Sub-pixel mapping of remote sensing images based on radial basis function interpolation","volume":"92","author":"Wang","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/JSTARS.2012.2227246","article-title":"Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery","volume":"6","author":"Xu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_29","first-page":"1851","article-title":"Using multiple subpixel shifted images with spatial-spectral information in soft-then-hard subpixel mapping","volume":"13","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/LGRS.2005.851551","article-title":"Superresolution mapping using Hopfield neural network with LIDAR data","volume":"2","author":"Nguyen","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/TGRS.2005.861752","article-title":"Superresolution mapping using a Hopfield neural network with fused images","volume":"44","author":"Nguyen","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6149","DOI":"10.1080\/01431161.2010.507797","article-title":"Super-resolution mapping using Hopfield neural network with panchromatic imagery","volume":"32","author":"Nguyen","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1016\/j.cageo.2007.05.010","article-title":"A linearised pixel swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery","volume":"33","author":"Thornton","year":"2007","journal-title":"Comput. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3293","DOI":"10.1080\/01431160500497127","article-title":"A sub-pixel mapping algorithm based on sub-pixel\/pixel spatial attraction models","volume":"27","author":"Mertens","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/01431161.2012.705441","article-title":"Sub-pixel mapping of remotely sensed imagery with hybrid intra- and inter-pixel dependence","volume":"34","author":"Ling","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4303","DOI":"10.1080\/01431161.2017.1317937","article-title":"Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images","volume":"38","author":"Wang","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A critical comparison among pansharpening algorithms","volume":"53","author":"Vivone","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/LGRS.2013.2262371","article-title":"Spectral unmixing model based on least squares support vector machine with unmixing residue constraints","volume":"10","author":"Wang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"57485","DOI":"10.1109\/ACCESS.2018.2873813","article-title":"Utilizing parallel networks to produce sub-pixel shifted images with multiscale spatio-spectral information for soft-then-hard sub-pixel mapping","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TGRS.2007.912448","article-title":"Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics","volume":"46","author":"Thomas","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1109\/TGRS.2015.2499790","article-title":"Enhanced sub-pixel mapping with spatial distribution patterns of geographical objects","volume":"54","author":"Ge","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TGRS.2012.2197860","article-title":"Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction","volume":"51","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TGRS.2007.907604","article-title":"Optimal MMSE pan sharpening of very high resolution multispectral images","volume":"46","author":"Garzelli","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/247\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:28:45Z","timestamp":1760185725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,26]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030247"],"URL":"https:\/\/doi.org\/10.3390\/rs11030247","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,1,26]]}}}