{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:59:44Z","timestamp":1776441584046,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,10,26]],"date-time":"2017-10-26T00:00:00Z","timestamp":1508976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.<\/jats:p>","DOI":"10.3390\/rs9111092","type":"journal-article","created":{"date-parts":[[2017,10,26]],"date-time":"2017-10-26T11:15:55Z","timestamp":1509016555000},"page":"1092","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Automatic Accurate High-Resolution Satellite Image Retrieval Method"],"prefix":"10.3390","volume":"9","author":[{"given":"Zhiwei","family":"Fan","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Dongying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Water Conservancy & Environment, Zhengzhou University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7224-677X","authenticated-orcid":false,"given":"Lingkui","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,26]]},"reference":[{"key":"ref_1","unstructured":"(2017, August 18). China Centre for Resources Satellite Data and Application. Available online: http:\/\/www.cresda.com."},{"key":"ref_2","unstructured":"(2017, August 18). NASA Landsat Program, Available online: https:\/\/landsat.gsfc.nasa.gov."},{"key":"ref_3","unstructured":"(2017, August 18). AIRBUS. Available online: http:\/\/www.intelligence-airbusds.com."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\u00b5S, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Laban, N., Nasr, A., ElSaban, M., and Onsi, H. (2012). Spatial Cloud Detection and Retrieval System for Satellite Images. Int. J. Adv. Comput. Sci. Appl., 3.","DOI":"10.14569\/IJACSA.2012.031235"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Surya, S.R., and Simon, P. (2013, January 15\u201317). Automatic Cloud Detection Using Spectral Rationing and Fuzzy Clustering. Proceedings of the 2013 2nd International Conference on Advanced Computing, Networking and Security (ADCONS), Mangalore, India.","DOI":"10.1109\/ADCONS.2013.44"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2013.02.019","article-title":"Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM\/ETM+ time series","volume":"134","author":"Goodwin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"776","DOI":"10.3390\/rs6010776","article-title":"Cloud and Cloud-Shadow Detection in SPOT5 HRG Imagery with Automated Morphological Feature Extraction","volume":"6","author":"Fisher","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/2150704X.2014.942921","article-title":"Automatic cloud detection for high spatial resolution multi-temporal images","volume":"5","author":"Han","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ba\u015feski, E., and Cenaras, \u00c7. (2015, January 16\u201319). Texture and color based cloud detection. Proceedings of the 2015 7th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey.","DOI":"10.1109\/RAST.2015.7208361"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4206","DOI":"10.1109\/JSTARS.2015.2438015","article-title":"Scene Learning for Cloud Detection on Remote-Sensing Images","volume":"8","author":"An","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2016.09.006","article-title":"Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction","volume":"121","author":"Wu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"22","article-title":"Cloud Index in Remote Sensing Image Based on GeoSOT","volume":"30","author":"An","year":"2014","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_16","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). (University of Toronto) Imagenet classification with deep convolutional neural networks. Proceedings of the NIPS\u201912 Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_17","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2017, August 18). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: https:\/\/arxiv.org\/abs\/1603.04467."},{"key":"ref_18","unstructured":"(2017, August 18). The CIFAR-10 Dataset. Available online: http:\/\/www.cs.toronto.edu\/~kriz\/cifar.html."},{"key":"ref_19","unstructured":"(2017, August 18). CIFAR-10 Network. Available online: https:\/\/github.com\/tensorflow\/models\/tree\/master\/tutorials\/image \/cifar10."},{"key":"ref_20","unstructured":"(2017, August 18). Geohash. Available online: http:\/\/geohash.org\/."},{"key":"ref_21","unstructured":"(2017, August 18). GF-1 Satellite. Available online: http:\/\/www.cresda.com\/EN\/satellite\/7155.shtml."},{"key":"ref_22","unstructured":"(2017, August 18). GF-2 Satellite. Available online: http:\/\/www.cresda.com\/EN\/satellite\/7157.shtml."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1092\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:35Z","timestamp":1760208515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,26]]},"references-count":22,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["rs9111092"],"URL":"https:\/\/doi.org\/10.3390\/rs9111092","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,26]]}}}