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best performance on almost all settings even if compared |
with the existing state-of-the-art methods. In addition, a |
series of findings of remote sensing pretraining will be |
presented, including the comparison with the traditional |
ImageNet pretraining and the performances on different |
downstream tasks. These findings provide useful insights |
WANG et al. : EMPIRICAL STUDY OF REMOTE SENSING PRETRAINING 3 |
for future research. |
The remainder of this paper is organized as follows. Section |
II introduces the related works, including the aerial scene |
recognition methods, especially CNN and vision transformer |
related ones, and the existing works of RSP. Section III |
describes the implementations of RSP, as well as the employed |
large capacity MillionAID dataset and the adopted ViTAE |
network. The experiment results on the four tasks and the |
related comprehensive analyses are presented in section IV . |
Finally, Section V concludes this paper. |
II. R ELATED WORK |
A. Aerial Scene Recognition |
There are a large number of CNN-based approaches to |
aerial scene recognition. Many off-the-shelf CNN classifica- |
tion models that are pretrained on ImageNet, such as the |
VGG [11], ResNet [12], and DenseNet [31], have been used |
and further finetuned on aerial images. Nonetheless, the chal- |
lenging aerial scene that possesses inter-class similarity and |
intra-class diversity can not be easily interpreted by only |
using features of the last layer, which are also considered as |
the “global features” compared with the features in previous |
layers, since it is also useful to highlight the important scene- |
relevant local regions for scene understanding. To address this |
issue, [23], [32] jointly exploits the multi-level CNN features, |
where the high-level features from deep layers usually have |
abundant semantic information, while the low-level features |
of the shallow layers tend to provide visual structures. For |
example, [32] conducts varied dilated convolutions on multiple |
features of VGG to obtain the more effective multiscale |
features. In addition, they optimize the category probabilities |
by preserving the local maximum and revising others with |
the two-dimensional Gaussian-like distribution in a window |
to strengthen the local regions. [23] separately applies graph |
convolutions on multilayer VGG features, where each pixel |
representation is regarded as a node, and the extracted graph |
features are concatenated with the last global visual features. |
Apart from feature fusion, the attention mechanisms have |
also been commonly used in aerial scene recognition since |
they can enhance the local features by simulating the human |
vision that directly assigns different weights to various areas of |
the current scene. The attention modules can be easily inserted |
into the CNN [33]. For example, [34] adopts channel attention |
and spatial attention modules in parallel like [35] to form a |
complementary attention. [32] also employs spatial attention |
to further adjust the optimized category probabilities. Another |
point for aerial scene recognition is to model the relationships |
of different regions. For example, [23] captures the topological |
relations of different objects, where the adjacency matrices are |
carefully designed. Besides, some interesting topics such as |
the multiple instance learning [36], self-distillation [25] and |
feature partition [26] have also been explored in aerial scene |
recognition research. |
Networks before linear layers in scene recognition task can |
be used as feature encoders for many downstream tasks, where |
the most representative ones for aerial images are semantic |
segmentation, object detection, and change detection. Whilesemantic segmentation and object detection are common tasks |
in CV , change detection is a specific task in RS. So far, a large |
number of related approaches in the aforementioned fields |
have been developed. Please refer to [37]–[40] for details. |
B. Vision Transformer |
Transformer is first proposed in [41], and has been widely |
used in the natural language processing (NLP) field [42], |
[43]. Besides NLP, the recently proposed vision transformers |
inspire a wave of researches [13], [14], [29], [44]–[51] in |
the CV field. The core component of the vision transformer |
is the MHSA, which is the extension of self-attention (SA). |
Compared with the convolution operation, the SA can capture |
long-range context and the relationships between any different |
positions. On the foundation of SA, the MHSA separately |
conducts the SAs in different projected subspaces, possess- |
ing more powerful representative abilities. ViT [44] is the |
pioneer vision transformer, where the input image is split |
into fixed-size patches to form tokens, which are then fed |
into the MHSA. However, the fixed receptive field restricts |
its applications on downstream tasks, and the global MHSA |
brings high computational complexities. To address the former |
issue, PVT [46] adopts the classical pyramid structure, improv- |
ing the model transferability with the generated hierarchical |
multiscale features. Swin [13] further substitutes the global |
MHSA to the shiftable window MHSA (WMHSA), it reduces |
the computational overhead significantly, achieving excellent |
performance on many CV tasks. Nonetheless, it still suffers |
from the common issues of vision transformers, such as being |
inefficient in modeling locality and scale invariance, which are |
exactly the advantages of CNN. Thus, besides Swin, we also |
employ another advanced vision transformer named ViTAE |
[14], [29], where the intrinsic biases of CNN are introduced |
into the transformer. It also adopts a pyramid structure to |
generate hierarchical features and local window attention, |
achieving better performance on many CV tasks while having |
a reasonable computational overhead and memory footprint. |
Using vision transformers as the backbone for aerial scene |
recognition is still under-explored, while the existing method |
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