DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS

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Conclusion Deep Learning can do the job ! ● Automatic adaptation to the context and good results ● Deeper is better... up to a limit ? Main issues : ● ● ● ● Expertise required Network architecture design Training procedures design Reduce the number of parameters
35 Future Work guideline Enhance architectures Siamese Networks Learning metrics from similarity measures SqueezeNet approach Get lighter models ! Adapt to new contexts The Sentinel Use case Switch to multispectral data Play with unlabelled data
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 and 16: Results : 6 layers deep net, 5*5 ne
Page 17 and 18: Results : 6 layers deep net, 5*5 ne
Page 19: Results : Accuracy vs training data
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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