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used, which is useful for feature discrimination. |
In recent years, with the superiority of automatically extract- |
ing deep features that reflect the inherent properties of objects, |
DL has achieved an impressive breakthrough in the computer |
vision (CV) field [11]–[17], as well as the RS field [18]– |
[20]. In the aerial scene recognition domain, the most com- |
monly used deep models are the convolutional neural networks |
(CNN), which have a good ability for local perception and |
global perception, where the former is achieved via applying |
sliding window convolutions on the input image to extract the |
local features while the latter is achieved by stacking multiple |
convolutional layers to increase the receptive field. According |
to the training scheme, the ways of using CNN in aerial scene |
recognition methods can be divided into three categories, i.e., |
training from scratch, finetuning, and adoption as the feature |
extractor. The first scheme does not involve any external data, |
meaning there is no prior knowledge that can be leveraged. |
To remedy this issue, the finetuning scheme uses the networks |
pretrained on a large-scale dataset as the start point for further |
training (i.e., finetuning). The last scheme directly extracts the |
feature from the pretrained CNN without further finetuning, |
therefore lacking the flexibility of adapting to aerial images |
from different downstream tasks. |
The existing literature [21] reveals that the finetuning strat- |
egy performs better than the other ones. We attribute it to the |
capacity of the used pretraining dataset, including the sample |
size and the number of categories. In the current RS field |
including the aerial scene recognition task, almost all finetuned |
models are pretrained on the ImageNet-1K dataset [22], which |
is the most famous image dataset in the CV field. Its millions |
of real-world images from 1,000 different categories enable |
the models to learn a powerful representation. Usually, off- |
the-shelf deep models like VGG [11] and ResNet [12] are |
used as backbone networks for aerial scene recognition, since |
training a new network on the ImageNet from scratch is |
time-consuming and requires a large number of computational |
resources. To further improve the classification performance, |
some methods [23], [24] adopt the ImageNet pretrained mod- |
els as the backbone network and employ multi-level features |
from it. In addition, many other components or strategies are |
specially designed for the aerial recognition task, such as the |
distillation [25] and feature partitioning [26]. |
Although the aforementioned methods have achieved re- |
markable performance for aerial scene recognition, there are |
still some issues needed to be further investigated. Intuitively, |
when considering the characteristics of aerial images, there |
exists a large domain gap compared with the natural images |
in terms of view, color, texture, layout, object, etc. Previous |
methods attempt to narrow this gap by further finetuning thepretrained model on the RS image dataset. Nevertheless, the |
systematic bias introduced by ImageNet Pretraining (IMP) |
has a non-negligible side impact on the performance [27]. |
On the other hand, we notice that there are abundant aerial |
images captured by diverse multiple sensors with the progress |
of RS technology, which can be used for pretraining. As a |
representative example, MillionAID [28] is so far the largest |
aerial image dataset and has a million-level volume similar to |
ImageNet-1K, making the Remote Sensing Pretraining (RSP) |
become possible. |
RSP enables training a deep model from scratch, implying |
that the candidate model is not necessary to be limited to |
the off-the-shelf CNN. In this paper, we also investigate the |
impact of RSP together with vision transformers, which have |
shown surprisingly good performance in the CV domain. |
Compared with convolutions in CNN which are skilled in |
modeling locality, the multi-head self-attention (MHSA) in |
transformers, e.g., Swin transformer [13], can flexibly capture |
diverse global contexts. Recently, ViTAE [14], [29] explores |
both convolutions and MHSA for modelling locality and |
long-range dependency simultaneously, achieving state-of-the- |
art (SOTA) performance on the ImageNet classification task |
and downstream vision tasks. In addition, it also extracts |
multi-scale features through a dilated convolution module and |
the stage-wise design, which have been shown effective in |
previous works, especially for aerial image interpretation [30]. |
Since both the CNN and aforementioned vision transformers |
can also produce intermediate features in different stages, |
which are useful for many downstream tasks, we also in- |
vestigate their finetuning performance of them after RSP on |
semantic segmentation, object detection, and change detection. |
To achieve these goals, we conduct extensive experiments on |
nine popular datasets and have some findings. The RSP is |
an emerging research direction in aerial image understanding, |
which however is still underexplored, especially in the context |
of vision transformers. We hope this study could fill this gap |
and provide useful insights for future research. |
The main contribution of this paper is three-fold. |
(1) We investigate the impact of remote sensing pretraining |
by training on a large-scale remote sensing dataset using |
three types of backbone networks, including traditional |
CNN, competitive visual transformer models, and the |
advanced ViTAE transformers. |
(2) We further finetune the above models that are initialized |
with the remote sensing or ImageNet pretraining weights |
on four kinds of tasks including scene recognition, se- |
mantic segmentation, object detection, and change detec- |
tion using a total of nine datasets, and compare them with |
other methods. |
(3) Experimental results show that typical vision transformer |
models can obtain competitive performance or perform |
better than CNN. Especially, the ViTAE achieves the |
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