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constructions. Compared with the IMP-ResNet-50, the RSP- |
ResNet-50 pays more attention to the important targets. It |
(a) |
(b) |
(c) |
(d) |
Fig. 5. (a), (b) and (c) are separately the training loss curves of ResNet-50, |
Swin-T and ViTAEv2-S, where the loss is recorded every 10 iterations. (d) |
is the testing accuracy curves of these models. All curves are obtained by |
training in the setting of UCM (8:2). The curves in (a), (b), and (c) have been |
smoothed by moving average. |
implies that RSP facilitates ResNet-50 to learn better semantic |
representations, probably by seeing semantic-similar images |
in the MillionAID dataset. Compared to the ResNet-50, the |
Swin-T has a better context modeling ability by attending |
faraway regions with the help of MHSA. Thus, their range |
of high response areas is wider. Surprisingly, the IMP-Swin-T |
mainly concentrates on background context, but the foreground |
responses have been enhanced when adopting the RSP. By |
combining the advantages of CNN and vision transformers, the |
ViTAEv2-S achieves a comprehensive perception of the whole |
scene. Especially, the RSP-ViTAEv2-S can better recognize |
the typical natural RS scenes, such as terrace, mountain and |
river. In the foreground object based scenes, compared with |
RSP-ResNet-50, the RSP-ViTAEv2-S not only focuses on the |
primary objects, but it also considers the related regions in |
the background. While on the objects, the RSP-ViTAEv2-S |
assigns higher attention, such as the airplane with a warmer |
color compared with IMP-ViTAEv2-S. In the residential areas |
with complex object distributions, the RSP-ViTAEv2-S can |
correctly capture the sparse buildings and connect these re- |
gions to form a holistic representation, effectively perceiving |
the overall information of the scene. In the first image of the |
school scene, the RSP-ViTAEv2-S simultaneously focuses on |
the playground and the surroundings, surpassing the SeCo- |
ResNet-50. As for the “school-2” image that is even difficult |
to be recognized by humans, these models show different |
recognition priorities. For example, the RSP-ViTAEv2-S not |
only considers the campus (it can be possibly distinguished by |
the irregular shape of buildings) like the IMP-ResNet-50, but |
also notices the surrounding roads. The results in Table V and |
Figure 4 validate the effectiveness of RSP and the superiority |
of vision transformers in the aerial scene recognition task. |
We also provide training loss curves and testing accuracy |
curves to investigate the performances of different pretraining |
10 JOURNAL OF L ATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 |
methods during the training phase. Here, the setting of UCM |
(8:2) is chosen as our example. The corresponding results |
have been displayed in Figure 5, where the loss curves of |
three kinds of networks are separately plotted. It can be seen |
that SeCo-ResNet-50 performs the worst and it has the largest |
initial loss, further confirming our aforementioned hypothesis |
that there is a large gap between Sentinel-2 multispectral |
images and the used RGB aerial images, although they are both |
RS images. Compared with the IMP, it can be observed that the |
RS pretrained models have better starting points, proving our |
intuition that the RS pretrained weights are easier to transfer |
between aerial scenes. It is also noteworthy that the shapes |
of these curves are similar for the same network, implying |
the unique characteristics of different network structures. We |
can also find the advanced structures enable ViTAEv2-S to |
reduce the performance gap indicated by the different starting |
points between IMP and RSP, while other networks failed. |
In addition, we can also find that the RSP accelerates the |
learning of ResNet-50 and makes the corresponding accuracy |
curve similar to Swin-T compared with IMP-ResNet-50. For |
Swin-T, the RSP also helps it converge fast. When adopting |
RSP, among all models, the advanced transformer network |
ViTAEv2-S can simultaneously achieve the best accuracy and |
the fastest convergence speed. |
B. Aerial Semantic Segmentation |
Aerial semantic segmentation is also a classification task |
like aerial scene recognition but at the pixel-level rather than |
the scene-level. We then evaluate the above three models on |
the aerial semantic segmentation task, including the scene |
parsing and object segmentation subtasks, where the former |
focuses on labeling each pixel of the whole scene, while the |
latter emphasizes the segmentation of foreground objects. |
1) Dataset: We use the ISPRS Potsdam dataset1and the |
large-scale segmentation benchmark — iSAID [81], to serve |
as the testbed of the corresponded subtasks, respectively. |
•Potsdam: This dataset is released by ISPRS Commission |
WG II/4. It covers a large scene that is collected over |
3.42 km2area of the Potsdam city. It contains 38 images, |
whose average size is 6,000 ×6,000 pixels, and the |
resolution is 0.5m. Among them, the training and testing |
sets separately have 24 and 14 images. There are 6 |
categories included in these scenes, namely impervious |
surface, building, low vegetation, tree, car, and clutter. |
•iSAID: This is a large-scale dataset that is mainly for in- |
stance segmentation. Also, it provides the semantic mask |
including 15 foregrounds and 1 background category over |
the aerial objects. It consists of 2,806 high-resolution |
images that range from 800 ×800 to 4,000 ×13,000 |
pixels. The training, validation, and test set separately |
have 1,411, 458, and 937 images. In this paper, only the |
validation set is used for evaluation since the testing set |
is unavailable. |
1https://www.isprs.org/education/benchmarks/UrbanSemLab/2d-sem-label- |
potsdam.aspx |
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