Datasets:
gremlin97
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RemoteSensingCorpus

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JOURNAL OF L ATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1
An Empirical Study of Remote Sensing Pretraining
Di Wang, Jing Zhang, Bo Du, Senior Member, IEEE, Gui-Song Xia, Senior Member, IEEE,
and Dacheng Tao, Fellow, IEEE
Abstract —Deep learning has largely reshaped remote sensing
(RS) research for aerial image understanding and made a
great success. Nevertheless, most of the existing deep models
are initialized with the ImageNet pretrained weights. Since
natural images inevitably present a large domain gap relative
to aerial images, probably limiting the finetuning performance
on downstream aerial scene tasks. This issue motivates us to
conduct an empirical study of remote sensing pretraining (RSP)