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Abstract
Semantic autoencoder, discriminative distance metric, zero-shot learning, remote sensing scene classification.
Highlights
R EMOTE sensing scene classification (RSSC) plays an important role in understanding the semantic content of scene images, and has been widely used in real-world applications such as land cover analysis and environmental monitoring [1]
In order to address the above issue, in this paper we propose a distance-constrained semantic autoencoder (DSAE)
We learn a semantic autoencoder for unseen scene classes, aiming to alleviate the domain shift problem
Summary
R EMOTE sensing scene classification (RSSC) plays an important role in understanding the semantic content of scene images, and has been widely used in real-world applications such as land cover analysis and environmental monitoring [1]. A large number of studies have improved the classification performance for remote sensing images [2]. With the rapid development of deep learning, RSSC has achieved a great success. A comprehensive review of recent achievements regarding deep learning for RSSC can be found in [3]. These deeplearning-based methods usually need a large number of labeled images and cannot classify new images from unseen scene classes.
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Schedule a callMore From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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