TY - GEN
T1 - A Tensor Decomposition Method for Unsupervised Feature Learning on Satellite Imagery
AU - Dehghanpoor, Golnoosh
AU - Frachetti, Michael
AU - Juba, Brendan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - We introduce a tensor factorization approach to unsupervised feature learning of hyper-spectral imagery, and demonstrate its effectiveness on land type classification of publicly available datasets. The results show that this approach can produce state of the art accuracy, compared to other methods for feature learning in the classification task.
AB - We introduce a tensor factorization approach to unsupervised feature learning of hyper-spectral imagery, and demonstrate its effectiveness on land type classification of publicly available datasets. The results show that this approach can produce state of the art accuracy, compared to other methods for feature learning in the classification task.
KW - feature learning
KW - hyperspectral imagery
KW - Tensor decomposition
UR - https://www.scopus.com/pages/publications/85102011694
U2 - 10.1109/IGARSS39084.2020.9324715
DO - 10.1109/IGARSS39084.2020.9324715
M3 - Conference contribution
AN - SCOPUS:85102011694
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1679
EP - 1682
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -