Deep neural network-based domain adaptation for classification of remote sensing images

Li Ma; Jiazhen Song

doi:10.1117/1.JRS.11.042612

20 September 2017 Deep neural network-based domain adaptation for classification of remote sensing images

Li Ma, Jiazhen Song

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 042612 (September 2017). https://doi.org/10.1117/1.JRS.11.042612

Abstract

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

Citation Download Citation

Li Ma and Jiazhen Song "Deep neural network-based domain adaptation for classification of remote sensing images," Journal of Applied Remote Sensing 11(4), 042612 (20 September 2017). https://doi.org/10.1117/1.JRS.11.042612

Received: 10 May 2017; Accepted: 22 August 2017; Published: 20 September 2017

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