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optimizer with the learning rate of 6e-5 and weight decay |
of 0.01. These models are trained for 200 epochs with a |
batch size of 8, while the learning rate is linearly decayed |
until the end of training. Following [140], the LEVIR dataset |
is clipped to the patches at the size of 256 ×256 with no |
overlaps. Thus, the sizes of the training, validation and testing |
set are 7,120/1,024/2,048. The final performance of different |
models is evaluated on the testing set, while the results on the |
validation set are only used to select the best model during |
training. We use the F1 score as the evaluation metric and the |
experiments are conducted on a single V100 GPU. |
3) Experimental Results: Quantitative Results and Anal- |
yses: The quantitative results are summarized in Table X. |
Without surprise, the self-supervised SeCo pretrained weights |
perform well on this task, e.g., the SeCo-ResNet-50 based |
BIT performs better than the IMP counterpart. Although the |
SeCo weights are trained to achieve seasonal invariance, the |
change features can be encoded via the multi-head sub-space |
embedding [58]. Nevertheless, ViTAEv2-S pretrained either by |
IMP or RSP performs better than SeCo-ResNet-50, showing |
the benefit of using the advanced backbone. |
Compared with other methods, it is no doubt that the |
ViTAEv2-S achieves the best performance, showing the po- |
tentiality of applying an advanced vision transformer on RS |
field. As before, we analyze the performance difference be- |
tween the RSP with the IMP through the perspective of task |
WANG et al. : EMPIRICAL STUDY OF REMOTE SENSING PRETRAINING 15 |
WANG et al. : EMPIRICAL STUDY OF REMOTE SENSING PRETRAINING 17 |
(a) |
(b) |
(c) |
(d) |
(e) |
(f) |
(g) |
(h) |
(i) |
(j) |
(k) |
(l) |
(m) |
(n) |
(o) |
(p) |
(q) |
(r) |
(s) |
(t) |
Fig. 9. Visualization of the change detection maps. The first and second row separately show the change detection results of a sample image from CDD and |
LEVIR dataset. Here, (a)(k), (b)(l) are the first and second temporals of the same regions. (c)(m) are the corresponded change annotations. (d)(n) are generated |
by the IMP-ResNet-50 based BIT, while (e)(o), (f)(p), (f)(o), (g)(q), (h)(r), (i)(s), (g)(t) are separately the results from the SeCo-ResNet-50, RSP-ResNet-50, |
IMP-Swin-T, RSP-Swin-T, IMP-ViTAE-S-Stage-Win and RSP-ViTAE-S-Stage-Win backbones. |
We hope this study can drive the works on aerial image field |
using vision transformers based on remote sensing pretraining. |
ACKNOWLEDGEMENT |
The authors would like to thank PhD candidate Yang Long |
and Prof. Gui-Song Xia for providing the MillionAID dataset |
and PhD candidate Qiming Zhang for offering the ViTAE |
series models. This work was done by Di Wang as the research |
intern in JD Explore Academy. |
REFERENCES |
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