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Airborne Object Detection Using Hyperspectral Imaging: Deep Learning Review
Authors
A Marinoni
A Plaza
+38 more
AA Nielsen
B Guo
CM Bachmann
CM Bachmann
DA Landgrebe
F Melgani
F Mirzapour
G Camps-Valls
G Camps-Valls
G Hughes
GE Hinton
H Zhang
JA Benediktsson
Jiang Xinhua
JM Bioucas-Dias
K Nogueira
L Bruzzone
L Ma
L Mou
L Na
MD Mura
N Srivastava
P Ghamisi
P. Zhong
PH Suen
R Archibald
R Ji
S Backer De
S Yu
TV Bandos
W Li
W Song
W Wang
Y Chen
Y Chen
Y Chen
Y Chen
Y Tarabalka
Publication date
1 January 2019
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
© 2019, Springer Nature Switzerland AG. Hyperspectral images have been increasingly important in object detection applications especially in remote sensing scenarios. Machine learning algorithms have become emerging tools for hyperspectral image analysis. The high dimensionality of hyperspectral images and the availability of simulated spectral sample libraries make deep learning an appealing approach. This report reviews recent data processing and object detection methods in the area including hand-crafted and automated feature extraction based on deep learning neural networks. The accuracy performances were compared according to existing reports as well as our own experiments (i.e., re-implementing and testing on new datasets). CNN models provided reliable performance of over 97% detection accuracy across a large set of HSI collections. A wide range of data were used: a rural area (Indian Pines data), an urban area (Pavia University), a wetland region (Botswana), an industrial field (Kennedy Space Center), to a farm site (Salinas). Note that, the Botswana set was not reviewed in recent works, thus high accuracy selected methods were newly compared in this work. A plain CNN model was also found to be able to perform comparably to its more complex variants in target detection applications
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OPUS - University of Technology Sydney
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Last time updated on 18/10/2019
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Last time updated on 10/08/2021