Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.isprsjprs.2018.03.006 |
Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification | |
Liu, Tao; Abd-Elrahman, Amr1,2 | |
通讯作者 | Abd-Elrahman, Amr |
来源期刊 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
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ISSN | 0924-2716 |
EISSN | 1872-8235 |
出版年 | 2018 |
卷号 | 139页码:154-170 |
英文摘要 | Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. |
英文关键词 | Deep learning Convolutional neural network Object-based classification Random forest Support vector machine small UAS OBIA |
类型 | Article |
语种 | 英语 |
国家 | USA |
收录类别 | SCI-E |
WOS记录号 | WOS:000431160100012 |
WOS关键词 | RESOLUTION IMAGERY ; FOREST BIOMASS ; LIDAR DATA ; VEGETATION ; SEGMENTATION ; REPRESENTATION ; ALGORITHM ; MODELS ; DESERT ; BRDF |
WOS类目 | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/210336 |
作者单位 | 1.Univ Florida, Sch Forest Resources & Conservat, Gainesville, FL 32611 USA; 2.Univ Florida, Gulf Coast Res Ctr, Plant City, FL 33563 USA |
推荐引用方式 GB/T 7714 | Liu, Tao,Abd-Elrahman, Amr. Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification[J],2018,139:154-170. |
APA | Liu, Tao,&Abd-Elrahman, Amr.(2018).Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,139,154-170. |
MLA | Liu, Tao,et al."Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 139(2018):154-170. |
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