DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS

23.04.2016 • Views

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DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS

D2_1710_03_Benoit

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DL architecture<br />

Cascading 3D convolutions, 1D convolutions and final fully connected layers

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gbdxtools Documentation

AN-M-PA-SPAR-2015.12.4

Design of X-band GaN combined power amplifier for radar applications

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Maxim Khodas

index.php?plugin=attach&refer=Appl Math&openfile=Oda09

wireless-communication-and-rf-system-design-using-matlab-and-simulink

kyushu07

04gun_01hen_11

e78fbee4bba3e6809de683b3

GRARA_Meets_Arduino

4_2_dry_etch

20100226_sawada_03

excel-koku

Experimental dataset University of Pavia dataset Single image 610×340 pixels 103 bands 9 classes

DL architecture Cascading 3D convolutions, 1D convolutions and final fully connected layers

Page 1 and 2: DEEP LEARNING APPROACH FOR REMOTE S
Page 3 and 4: Scientific Context 350 millions pho
Page 5 and 6: Deep Learning Modelling high level
Page 7 and 8: Deep Learning Convolutional layer :
Page 9: DL for Hyperspectral Data Classific
Page 13 and 14: Results : accuracy vs complexity Ac
Page 15 and 16: Results : 6 layers deep net, 5*5 ne
Page 17 and 18: Results : 6 layers deep net, 5*5 ne
Page 19 and 20: Results : Accuracy vs training data
Page 21 and 22: 35 Future Work guideline Enhance ar
Page 23 and 24: Thank you for your attention
Page 25 and 26: 37 Future Work guideline Testing th
Page 27 and 28: 40 Future Work guideline Extending