DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS

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Results : 6 layers deep net, 5*5 neighbors Spectral profiles
Results : 6 layers deep net, 5*5 neighbors Per class accuracy mostly stable ~95% on average Classification errors explained by : • similar spectral profiles • boundary effects (ROI size vs neighborhood class)
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 and 10: DL for Hyperspectral Data Classific
Page 11 and 12: DL architecture Cascading 3D convol
Page 13 and 14: Results : accuracy vs complexity Ac
Page 15: 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

DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS

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