{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:28:17Z","timestamp":1772252897555,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"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>This paper proposed a new remote sensing observation capability evaluation model (RSOCE) based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites, such as China\u2019s resource satellite series and GF-4, as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation, and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites.<\/jats:p>","DOI":"10.3390\/rs13091717","type":"journal-article","created":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T10:30:41Z","timestamp":1619692241000},"page":"1717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluation Model of Remote Sensing Satellites Cooperative Observation Capability"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhonggang","family":"Zheng","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Beijing Institute of Remote Sensing Information, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-9660","authenticated-orcid":false,"given":"Qingmei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Fu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Katona, Z., Kourogiorgas, C., Panagopoulos, A., and Jeannin, N. (2015). Capacity analysis of high-throughput satellite links for Earth observation missions. Int. J. Satell. Commun. Netw., 33.","DOI":"10.1002\/sat.1125"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, S., Xiao, Y., Yang, P., Liu, Y., Chang, W., and Zhou, S. (2019). An effectiveness evaluation model for satellite observation and data-downlink scheduling considering weather uncertainties. Remote Sens., 11.","DOI":"10.3390\/rs11131621"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dana, I., Moise, C., Laz\u0103r, A., Ri\u0219cu\u021ba, N., Cristescu, C., Dedulescu, A.L., Mihalache, C.E., and Badea, A. (2020). Satellite remote sensing for the analysis of the micia and germisara archaeological sites. Remote Sens., 12.","DOI":"10.3390\/rs12122003"},{"key":"ref_4","unstructured":"Gang, X., Gao, J., Hu, B., Liu, Y., and Wang, J. (2014, January 26\u201328). Network traffic load balancing method with geo satellites cooperation. Proceedings of the International Conference on Wireless Communications, Beijing, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Guo, X.B., Zhou, H.B., and Gang, L. (2015, January 15\u201317). Service oriented cooperation architecture for distributed satellite networks. Proceedings of the 2015 International Conference on Wireless Communications & Signal Processing (WCSP), Nanjing, China.","DOI":"10.1109\/WCSP.2015.7341218"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yanagimachi, T., and Ishida, Y. (2016, January 16\u201319). Effect of formation control for multiple satellite cooperation system. Proceedings of the 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA), Penang, Malaysia.","DOI":"10.1109\/ICAICTA.2016.7803093"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, H., Lei, P., Zhang, L., Xin, H., and Wei, L. (2014, January 13\u201318). A cooperation earth observation model of SAR satellite and optical remote sensing satellite. Proceedings of the IGARSS 2014\u20142014 IEEE International Geoscience and Remote Sensing Symposium, Quebec, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946490"},{"key":"ref_8","unstructured":"Zhang, W.B., Liu, Z.G., Sun, P., and Xu, H. (2010, January 16\u201318). An intrusion detection model for satellite network. Proceedings of the IEEE International Conference on Information Management and Engineering, Chengdu, China."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Yu, N. (2015, January 12\u201313). Self-Assembly method for cooperation of multi-satellites. Proceedings of the 2015 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2015.251"},{"key":"ref_10","unstructured":"Shi-Xing, W., Jin-Hua, W., and Liang, T. (2012, January 25\u201327). Task allocation for multi-satellite cooperation based on estimation of distribution algorithm. Proceedings of the 31st Chinese Control Conference, Hefei, China."},{"key":"ref_11","first-page":"256","article-title":"Research on satisfied degree evaluation method for satellite imaging reconnaissance requirement","volume":"38","author":"Dishan","year":"2012","journal-title":"Comput. Eng."},{"key":"ref_12","first-page":"299","article-title":"Fuzzy evaluation of application satisfaction for remote sensing satellite","volume":"33","author":"Wu","year":"2015","journal-title":"J. Appl. Sci."},{"key":"ref_13","unstructured":"Lubbe, J.C.A., and Backer, E. (1995, January 17\u201320). Hierarchical classification inference for fuzzy data analysis. Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society\u2014NAFIPS, College Park, MD, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3346","DOI":"10.1109\/TGRS.2013.2272581","article-title":"Supervised classification of multisensor and multiresolution remote sensing images with a hierarchical copula-based approach","volume":"52","author":"Voisin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1109\/TAC.2017.2762468","article-title":"Hierarchical robust performance analysis of uncertain large scale systems","volume":"63","author":"Laib","year":"2018","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yuan, Y., Feng, Y., and Lu, X. (2019). Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection. IEEE Trans. Geosci. Remote Sens., 1\u201314.","DOI":"10.1109\/TGRS.2019.2900302"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rongjie, L., Jie, Z., Pingjian, S., Fengjing, S., and Guanfeng, L. (2008, January 12\u201314). An Agglomerative Hierarchical Clustering Based High-Resolution Remote Sensing Image Segmentation Algorithm. Proceedings of the 2008 International Conference on Computer Science and Software Engineering, Wuhan, China.","DOI":"10.1109\/CSSE.2008.1017"},{"key":"ref_18","unstructured":"He, D. (2014). Initial efficiency evaluation approach for space-based earth observation satellites system. Chin. Space Sci. Technol., 34."},{"key":"ref_19","unstructured":"CNSA (2021, January 18). China High-Resolution Earth Observation System (CHEOS) and Its Latest Development. Available online: http:\/\/www.oosa.unvienna.org\/pdf\/pres\/stsc2014\/tech-47E.pdf."},{"key":"ref_20","first-page":"1191","article-title":"Performance assessment of atmospheric correction for multispectral Data of GF-4 on Inland Case \u2161 Turbid Water","volume":"38","author":"Song","year":"2018","journal-title":"Guang Pu Xue Yu Guang Pu Fen Xi\/Spectrosc. Spectr. Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1717\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:55:12Z","timestamp":1760162112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,29]]},"references-count":20,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091717"],"URL":"https:\/\/doi.org\/10.3390\/rs13091717","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202102.0251.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,29]]}}}