20 September 2017 Deep neural network-based domain adaptation for classification of remote sensing images
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J. of Applied Remote Sensing, 11(4), 042612 (2017). doi: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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Li Ma, 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). http://dx.doi.org/10.1117/1.JRS.11.042612 Submission: Received 10 May 2017; Accepted 22 August 2017
Submission: Received 10 May 2017; Accepted 22 August 2017
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KEYWORDS
Neural networks

Remote sensing

Simulation of CCA and DLA aggregates

Image classification

Detection and tracking algorithms

Composites

Image fusion

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