DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS

Results :
Accuracy vs training dataset size
Accuracy
102
Accuracy on Pavia University dataset
100
98
96
94
92
90
6 layers, 3*3 neighbors, ~4419 parameters
6 layers, 5*5 neighbors, ~6074 parameters
88
0 10 20 30 40 50 60 70 80 90 100
Training samples ratio (%)
CNN challenger, 5*5
neighbors, no pretraining
K. Makantasis&al “Deep
supervised learning for
hyperspectral data
classification through
convolutional neural
networks,” IGRS2015
~20000 parameters
SAE challenger, 7*7
neighbors, with pretraining
X. Ma&al“Hyperspectral image
classification via
contextual deep learning,”
EURASIP JIVP 2015
>>20000 parameters