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More recently, fusion\u2010based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion\u2010based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU\u2010RS19, and NWPU\u2010RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance.<\/jats:p>","DOI":"10.1155\/2021\/6659831","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T23:05:55Z","timestamp":1623279955000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Remote Sensing Image Scene Classification Based on Fusion Method"],"prefix":"10.1155","volume":"2021","author":[{"given":"Liancheng","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1243-0123","authenticated-orcid":false,"given":"Keming","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2017.2675998"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00130487"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431160903475266"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.881741"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2268736"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2796069"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs8060483"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40261-6_59"},{"key":"e_1_2_10_9_2","doi-asserted-by":"crossref","unstructured":"YangY.andNewsamS. 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