Multisource Remote Sensing Data Classification Using Deep Hierarchical Random Walk Networks

Xudong Zhao; Ran Tao; Wei Li

ICASSP 2019accepted0 citations

Multisource Remote Sensing Data Classification Using Deep Hierarchical Random Walk Networks

Abstract

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.

BibTeX

@inproceedings{icassp2019_multisourceremot,
  title = {Multisource Remote Sensing Data Classification Using Deep Hierarchical Random Walk Networks},
  author = {Xudong Zhao and Ran Tao and Wei Li},
  booktitle = {ICASSP 2019},
  year = {2019}
}