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Jihyoung Ryu and Yeongmin Jang
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture d...
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Yifan Si, Dawei Gong, Yang Guo, Xinhua Zhu, Qiangsheng Huang, Julian Evans, Sailing He and Yaoran Sun
DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dim...
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Shiuan Wan, Mei-Ling Yeh and Hong-Lin Ma
Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the re...
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Joongbin Lim, Kyoung-Min Kim and Ri Jin
Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species cla...
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Bethany Melville, Arko Lucieer and Jagannath Aryal
This paper presents the results of a study undertaken to classify lowland native grassland communities in the Tasmanian Midlands region. Data was collected using the 20 band hyperspectral snapshot PhotonFocus sensor mounted on an unmanned aerial vehicle....
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Zahra Dabiri and Stefan Lang
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L. Jiang, B. Zhu, H. Jing, X. Chen, X. Rao, Y. Tao
Pág. 153 - 160
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Chang, C.-I. Chiang, S.-S.
Pág. 1314 - 1325
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Chang, C.-I.
Pág. 1065 - 1081
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Chang, C.-I. Ren, H. Chiang, S.-S.
Pág. 760 - 768
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