TY - GEN
T1 - Cross-Scene Hyperspectral Image Classification Based on Deep Conditional Distribution Adaptation Networks
AU - Geng, Jie
AU - Ma, Xiaorui
AU - Jiang, Wen
AU - Hu, Xiaoyu
AU - Wang, Dawei
AU - Wang, Hongyu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Cross-scene classification of hyperspectral image (HSI) has been increasingly researched due to its crucial utilization in practical applications. However, cross-scene data generally perform distribution discrepancy, which hampers the transfer learning performance. To address this issue, deep conditional distribution adaptation networks (DCDAN) are proposed for HSI cross-scene classification, which aim to reduce the distribution shift between a source domain and a target domain. The proposed deep network adopts a conditional constraint to match the class conditional distributions across domains, where a great number of training samples from the source domain and a small number of training samples from the target domain are utilized to train the deep model. Cross-scene classification results on two HSIs demonstrate that the proposed network is able to yield superior performance compared with some related methods.
AB - Cross-scene classification of hyperspectral image (HSI) has been increasingly researched due to its crucial utilization in practical applications. However, cross-scene data generally perform distribution discrepancy, which hampers the transfer learning performance. To address this issue, deep conditional distribution adaptation networks (DCDAN) are proposed for HSI cross-scene classification, which aim to reduce the distribution shift between a source domain and a target domain. The proposed deep network adopts a conditional constraint to match the class conditional distributions across domains, where a great number of training samples from the source domain and a small number of training samples from the target domain are utilized to train the deep model. Cross-scene classification results on two HSIs demonstrate that the proposed network is able to yield superior performance compared with some related methods.
KW - classification
KW - deep neural networks
KW - Domain adaptation
KW - hyperspectral image (HSI)
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85077725253&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8899197
DO - 10.1109/IGARSS.2019.8899197
M3 - 会议稿件
AN - SCOPUS:85077725253
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 716
EP - 719
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -