Journal
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Volume -, Issue -, Pages 2187-2191Publisher
IEEE
DOI: 10.1109/icassp.2019.8683032
Keywords
Hierarchical random walk; convolutional neural network (CNN); hyperspectral image (HSI); multi-source remote sensing classification
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Funding
- National Natural Science Foundation of China [61331021, 61421001, 91638201, U1833203]
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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.
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