TY - GEN
T1 - Polsar Image Classification via Complex-Valued Multi-Scale Convolutional Neural Network
AU - Zhang, Lamei
AU - Zhang, Siyu
AU - Dong, Hongwei
AU - Lu, Da
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Convolutional neural networks (CNNs) have achieved promising results in polarimetric SAR image classification. Generally, the semantic segmentation of a large image is conducted using the image slices, for which the small slices may be in a single pure class but with insufficient information, and the large ones may contain the mixed class. Therefore, a complex-valued multi-scale CNN (CVMS-CNN) architecture is proposed to extract the hierarchical multi-scale information, i.e. local and global features, and adapt to the complex PolSAR data format, simultaneously. Moreover, the optimal feature fusion mechanism is given through comprehensive comparisons. Experiments are carried out on two benchmark datasets to verify the effectiveness. Numerical simulations show that the classification results have been significantly improved via CVMS-CNN compared with the state-of-the-arts.
AB - Convolutional neural networks (CNNs) have achieved promising results in polarimetric SAR image classification. Generally, the semantic segmentation of a large image is conducted using the image slices, for which the small slices may be in a single pure class but with insufficient information, and the large ones may contain the mixed class. Therefore, a complex-valued multi-scale CNN (CVMS-CNN) architecture is proposed to extract the hierarchical multi-scale information, i.e. local and global features, and adapt to the complex PolSAR data format, simultaneously. Moreover, the optimal feature fusion mechanism is given through comprehensive comparisons. Experiments are carried out on two benchmark datasets to verify the effectiveness. Numerical simulations show that the classification results have been significantly improved via CVMS-CNN compared with the state-of-the-arts.
KW - convolutional neural networks
KW - deep learning
KW - multi-scale
KW - polarimetric SAR image classification
UR - https://www.scopus.com/pages/publications/85101964601
U2 - 10.1109/IGARSS39084.2020.9323621
DO - 10.1109/IGARSS39084.2020.9323621
M3 - 会议稿件
AN - SCOPUS:85101964601
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 200
EP - 203
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 -