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merely takes ViT as a branch parallel to CNN [52], implying |
an urgent need to further explore the applications of vision |
transformers on aerial scene tasks. |
C. Remote Sensing Pretraining |
Pretraining using RS dataset for aerial scene recognition is |
a very intuitive idea. However, to our best knowledge, there |
are few explorations in this direction since the insufficiency |
of large-scale RS datasets like ImageNet. Nevertheless, re- |
searchers have attempted to obtain the RS representations from |
other resources. For example, GeoKR [53] leverages the global |
land cover product as the labels, and they use the mean-teacher |
framework to alleviate the influences of imaging time and res- |
olution differences between RS images and geographical ones. |
However, forcing alignment of different datasets inevitably |
brings errors due to the intrinsic different data distributions. |
The scarcity of large capacity RS dataset is mainly in the |
aspect of category labels instead of images. In this case, it |
4 JOURNAL OF L ATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 |
Reduction CellReduction CellReduction CellNormal CellNormal CellP |
1/4 1/8 1/16 |
Class tokenConcatLinear |
AdditionSchool |
Reduction Cell |
1/4 1/8 1/16Linear |
School |
Normal CellReduction CellNormal CellReduction CellNormal CellReduction CellNormal Cell |
1/32GAP(a) |
(b)×𝑵𝟏 ×𝑵𝟐 ×𝑵𝟑 ×𝑵𝟒𝑵 |
Fig. 2. The diagram of the adopted ViTAE models. (a) Original ViTAE [14]. (b) ViTAEv2 [29]. |
is promising to develop self-supervised pretraining methods |
[54]–[57] and some related methods have been developed in |
the RS area [27], [58]–[60]. For instance, SeCo [58] leverages |
the seasonal changes to enforce consistency between positive |
samples, which are the unique characteristics of aerial scenes, |
while [59] simultaneously fuses the temporal information and |
geographical location into the MoCo-V2 [55] framework. |
Moreover, the channel properties [60] and spatial variance |
[27] are also explored in some approaches. In this study, |
since the adopted MillionAID dataset has the ground truth |
labels annotated by experts and does not contain any temporal |
information, we directly conduct the supervised pretraining |
like the conventional IMP |
III. R EMOTE SENSING PRETRAINING |
In this section, we first provide a brief introduction of |
the adopted large-scale RS dataset—MillionAID. Then, we |
describe the details of the utilized ViTAE transformer. The |
whole RSP procedure will be presented finally. |
A. MillionAID |
To our best knowledge, the MillionAID is by far the largest |
dataset in the RS area. It contains 100,0848 non-overlapping |
scenes, exceeding the competitive counterpart fMoW [61] |
and BigEarthNet [62], which separately includes 132,716 |
and 590,326 scenes. Note that the fMoW contains 1,047,691 |
images since they provide multiple temporal views for each |
scene. In addition, it should be noted that the fMoW and |
BigEarthNet are multispectral datasets, while the MillionAID |
is an RGB dataset, which is more suitable for existing deep |
vision models. The categories of MillionAID consist of a |
hierarchical tree that has 51 leaves, which locate on 28 parent |
nodes on the second level, while the 28 groups are belonging |
to 8 base classes: agriculture land, commercial land, industrial |
land, public service land, residential land, transportation land, |
unutilized land, and water area, and each leaf category has |
about 2,000 ∼45,000 images. This dataset is obtained from the |
Google Earth that is made up of diverse sensors including but |
not limited to SPOT, IKONOS, WorldView, and Landsat series,resulting in different resolutions. The maximum resolution can |
reach 0.5m, while the smallest is 153m. The image size ranges |
from 110 ×110 to 31,672 ×31,672. |
B. ViTAE |
The original ViTAE [14] follows the deep-narrow design |
of T2T-ViT [63], which found that simply decreasing channel |
dimensions and increasing layer depth can improve the feature |
richness of ViT, boosting the performance while reducing the |
model size and computational cost. Thus, the original ViTAE |
firstly downsamples the input image to 1/16 size by three |
reduction cells. Similar to ViT, a class token is concatenated |
with the output of the third reduction cell before adding |
an element-wise sinusoid position encoding. Then, multiple |
normal cells are stacked, and the feature size is always kept |
till the end. The class token feature of the last normal cell is |
used for classification through a linear layer. |
Although the original ViTAE performs well on ImageNet |
classification, it is unsuitable for transferring to other tasks |
like segmentation, detection, pose estimation, and so on, since |
it cannot generate abundant intermediate features at different |
scales. Thus, the authors propose the ViTAEv2 variant [29], |
which adopts the classic stage-wise design of popular back- |
bone networks such as ResNet and Swin. Figure 2 shows |
the comparison between the original ViTAE and ViTAEv2. |
In ViTAEv2, the network is split into multiple stages, usually, |
the number is 4. In each stage, the first cell is a reduction cell |
for downsampling, which is followed by the stacked normal |
cells. An average pooling layer is used after the last normal cell |
to replace the class tokens. When finetuning on downstream |
tasks, this pooling layer is removed and the remained network |
is connected with corresponding task decoders. |
In this paper, we employ the ViTAEv2 model for RSP. |
Concretely, inspired by Swin [13], some MHSAs in ViTAE are |
replaced by the WMHSA to reduce computational cost. Specif- |
ically, considering the feature size becomes smaller in later |
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