TY - GEN
T1 - Multisource Remote Sensing Data Classification Using Deep Hierarchical Random Walk Networks
AU - Zhao, Xudong
AU - Tao, Ran
AU - Li, Wei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Collaborative classification of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data is investigated using effective hierarchical random walk networks, denoted as HRWN. The proposed HRWN jointly optimizes dual-tunnel CNN, pixelwise affinity and seeds map via a novel random walk layer, which enforces spatial consistency in the deepest layers of the network. In designed random walk layer, the predicted distribution of dual-tunnel CNN serves as global prior while pixelwise affinity reflects local similarity of pixel pairs, which preserves boundary localization and spatial consistency well. Experimental results validated with two real multisource remote sensing data demonstrate that the proposed HRWN can significantly outperform other state-of-art methods.
AB - Collaborative classification of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data is investigated using effective hierarchical random walk networks, denoted as HRWN. The proposed HRWN jointly optimizes dual-tunnel CNN, pixelwise affinity and seeds map via a novel random walk layer, which enforces spatial consistency in the deepest layers of the network. In designed random walk layer, the predicted distribution of dual-tunnel CNN serves as global prior while pixelwise affinity reflects local similarity of pixel pairs, which preserves boundary localization and spatial consistency well. Experimental results validated with two real multisource remote sensing data demonstrate that the proposed HRWN can significantly outperform other state-of-art methods.
KW - Hierarchical random walk
KW - convolutional neural network (CNN)
KW - hyperspectral image (HSI)
KW - multi-source remote sensing classification.
UR - https://www.scopus.com/pages/publications/85068968778
U2 - 10.1109/ICASSP.2019.8683032
DO - 10.1109/ICASSP.2019.8683032
M3 - Conference contribution
AN - SCOPUS:85068968778
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2187
EP - 2191
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -