TY - GEN
T1 - A deep spatial/spectral descriptor of hyperspectral texture using scattering transform
AU - Franchi, Gianni
AU - Angulo, Jesus
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - A technique to describe the spatial / spectral features of hyperspectral images is introduced. These descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations, so called spatial invariances. Moreover, we also consider spectral invariances which are related to the composition of the pixels. Our approach is based on the scattering transform, which provides an useful framework for deep learning classification. The goal through these descriptors is to improve pixel-wise classification of hyperspectral images.
AB - A technique to describe the spatial / spectral features of hyperspectral images is introduced. These descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations, so called spatial invariances. Moreover, we also consider spectral invariances which are related to the composition of the pixels. Our approach is based on the scattering transform, which provides an useful framework for deep learning classification. The goal through these descriptors is to improve pixel-wise classification of hyperspectral images.
KW - Computer vision
KW - Deep learning
KW - Hyperspectral images
KW - Pixel-wise classification
KW - Wavelet transform
UR - https://www.scopus.com/pages/publications/85006716773
U2 - 10.1109/ICIP.2016.7533024
DO - 10.1109/ICIP.2016.7533024
M3 - Conference contribution
AN - SCOPUS:85006716773
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3568
EP - 3572
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -