Remote Sensing Image Scene Classification Based on Fusion Method
Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image...
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2021-01-01
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Series: | Journal of Sensors |
Online Access: | http://dx.doi.org/10.1155/2021/6659831 |
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doaj-3dd6ae095f314e1a8ab61ee753305d582021-06-21T02:24:39ZengHindawi LimitedJournal of Sensors1687-72682021-01-01202110.1155/2021/6659831Remote Sensing Image Scene Classification Based on Fusion MethodLiancheng Yin0Peiyi Yang1Keming Mao2Qian Liu3College of SoftwareCollege of Computer ScienceCollege of SoftwareCollege of SoftwareRemote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based 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-RS19, and NWPU-RESISC45 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.http://dx.doi.org/10.1155/2021/6659831 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liancheng Yin Peiyi Yang Keming Mao Qian Liu |
spellingShingle |
Liancheng Yin Peiyi Yang Keming Mao Qian Liu Remote Sensing Image Scene Classification Based on Fusion Method Journal of Sensors |
author_facet |
Liancheng Yin Peiyi Yang Keming Mao Qian Liu |
author_sort |
Liancheng Yin |
title |
Remote Sensing Image Scene Classification Based on Fusion Method |
title_short |
Remote Sensing Image Scene Classification Based on Fusion Method |
title_full |
Remote Sensing Image Scene Classification Based on Fusion Method |
title_fullStr |
Remote Sensing Image Scene Classification Based on Fusion Method |
title_full_unstemmed |
Remote Sensing Image Scene Classification Based on Fusion Method |
title_sort |
remote sensing image scene classification based on fusion method |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-7268 |
publishDate |
2021-01-01 |
description |
Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based 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-RS19, and NWPU-RESISC45 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. |
url |
http://dx.doi.org/10.1155/2021/6659831 |
work_keys_str_mv |
AT lianchengyin remotesensingimagesceneclassificationbasedonfusionmethod AT peiyiyang remotesensingimagesceneclassificationbasedonfusionmethod AT kemingmao remotesensingimagesceneclassificationbasedonfusionmethod AT qianliu remotesensingimagesceneclassificationbasedonfusionmethod |
_version_ |
1721369212999434240 |