gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
224px, .7m GSD
Ground Truth Image Ihr
(448px, .3m GSD)resampleMAE Encoder
Transformers
14 14GSDPE
L1 loss
448224
Scale-MAE Decoder
L2 loss
Loss
Upsampling
Reconstruction
28
56
Demask
High freq.
Ground Truth
LB
Low freq.
Ground TruthDeconv
DeconvLB
Ground Truth Image I hr
(448px, .3m GSD)
DecodingFigure 2. Scale-MAE employs the Masked Autoencoder framework. An input image is patchified and masked before being passed into an
MAE encoder. A Ground Sample Distance Positional Encoding (GSDPE) is added to the encoder input, which scales the positional encodings
to the area of ground covered. The Scale-MAE decoders has three stages: (1) Decoding, which uses a smaller number of transformer layers
than MAE to decode the encoded values (2) Upsampling, which progressively deconvolves the decoded feature map to a larger size before
being passed through the Laplacian Blocks (abbreviated LB, see Section 3), (3) Reconstruction, which then reconstructs low and high
frequency features at different scales. These outputs are used to compute an aggregate loss with ground truth low and high frequency features,
where following super resolution literature [2], an L1 loss is used for high frequency output to better reconstruct edges and an L2 loss is used
for low frequency output to better reconstruct average values.
data at a specific scale [13, 20, 22, 32, 41]. In this paper we
present Scale-MAE , a masked reconstruction model that ex-
plicitly learns relationships between data at different, known
scales throughout the pretraining process. By leveraging this
information, Scale-MAE produces a pretrained model that
performs better across a wide range of GSDs and tasks.
Masked Autoencoders [26] offer self-supervised learn-
ing without explicit augmentations. A standard Masked
Autoencoder resizes/crops an image, masks the majority of
the transformed image, and then tasks a Vision Transformer
(ViT) based autoencoder with embedding the unmasked com-
ponents. A decoding ViT then decodes the full image from
these learned embeddings, where the decoder is later dis-
carded and the encoder is used to produce representations
for an unmasked input image.
Existing MAE-based pretraining approaches fail to gen-
eralize across domains with images at multiple scales.
Scale-MAE (Figure 1) overcomes this through a GSD-based
positional encoding derived from the land area covered in the
image. This informs the ViT of both the position and scale of
the input image. Scale-MAE also uses a Laplacian-pyramid
decoder to encourage the network to learn multiscale rep-
resentations. The embeddings are decoded to two images
containing low and residual high frequency information, re-
spectively – see Figure 2. As we discuss in Section 3, this
structure allows the ViT decoder to use fewer parameters
than MAE while still producing strong representations across
multiple scales.
We show that Scale-MAE leads to better performing,
more robust multiscale representations than both a stan-
dard MAE and a recently proposed, state-of-the-art MAEs
SatMAE [13] and ConvMAE [21] across remote sensing
datasets with a variety of scale and resolution characteristics.
To the best of our knowledge Scale-MAE is the first self-supervised MAE to include scale-aware positional encoding
and Laplacian pyramids. In our experiments, Scale-MAE
achieves an average of a 5.6%nonparametric kNN classifica-
tion improvement across eight remote sensing datasets com-
pared to current state-of-the-art in addition to a 0.9mIoU
to1.7mIoU improvement on the SpaceNet building seg-
mentation transfer task for a range of evaluation scales (see
Figure 1).
2. Related Work
Representation learning and the Masked Autoencoder.
Representation learning aims to extract meaningful, intrin-
sic features from data for downstream use [5]. In prac-
tice, this often entails pretraining a deep network so that
a lightweight learning routine can then finetune it for a par-
ticular downstream task, see [15,16,17,24,27,30,37,49,66].
The Masked Autoencoder (MAE) is a recent state-of-the-art
self-supervised representation learning method in computer
vision that pretrains a ViT encoder by masking an image,
feeding the unmasked portion into a transformer-based en-
coder, and then tasking the decoder with reconstructing the
input image [26]. MAEs fail to leverage scale information in
scale-dependent domains as they are often reliant on absolute
or relative positional encodings. To the best of our knowl-
edge, Scale-MAE is the first MAE-based self-supervised
learning method to incorporate a scale-variant positional
encoding.
Remote Sensing Representation Learning Neumann et
al. [46] were one of the first to exhaustively share results on
existing representation learning and semi-supervised learn-
ing techniques for remote sensing imagery. Gao et al. [22]
demonstrated the effectiveness of MAE pretraining for re-
mote sensing image classification. Ayush et al. [3] lever-
aged the metadata from remote sensing images via spatially
aligned but temporally separated images as positive pairs
for contrastive learning and predicted the latitude and longi-
tude as pretext tasks. Gupta et al. [25] demonstrated the use

224px, .7m GSD

Ground Truth Image Ihr

(448px, .3m GSD)resampleMAE Encoder

Transformers

14 14GSDPE

L1 loss

448224

Scale-MAE Decoder

L2 loss

Loss

Upsampling

Reconstruction

28

56

Demask

High freq.

Ground Truth

LB

Low freq.

Ground TruthDeconv

DeconvLB

Ground Truth Image I hr

(448px, .3m GSD)

DecodingFigure 2. Scale-MAE employs the Masked Autoencoder framework. An input image is patchified and masked before being passed into an

MAE encoder. A Ground Sample Distance Positional Encoding (GSDPE) is added to the encoder input, which scales the positional encodings

to the area of ground covered. The Scale-MAE decoders has three stages: (1) Decoding, which uses a smaller number of transformer layers

than MAE to decode the encoded values (2) Upsampling, which progressively deconvolves the decoded feature map to a larger size before

being passed through the Laplacian Blocks (abbreviated LB, see Section 3), (3) Reconstruction, which then reconstructs low and high

frequency features at different scales. These outputs are used to compute an aggregate loss with ground truth low and high frequency features,

where following super resolution literature [2], an L1 loss is used for high frequency output to better reconstruct edges and an L2 loss is used

for low frequency output to better reconstruct average values.

data at a specific scale [13, 20, 22, 32, 41]. In this paper we

present Scale-MAE , a masked reconstruction model that ex-

plicitly learns relationships between data at different, known

scales throughout the pretraining process. By leveraging this

information, Scale-MAE produces a pretrained model that

performs better across a wide range of GSDs and tasks.

Masked Autoencoders [26] offer self-supervised learn-

ing without explicit augmentations. A standard Masked

Autoencoder resizes/crops an image, masks the majority of

the transformed image, and then tasks a Vision Transformer

(ViT) based autoencoder with embedding the unmasked com-

ponents. A decoding ViT then decodes the full image from

these learned embeddings, where the decoder is later dis-

carded and the encoder is used to produce representations

for an unmasked input image.

Existing MAE-based pretraining approaches fail to gen-

eralize across domains with images at multiple scales.

Scale-MAE (Figure 1) overcomes this through a GSD-based

positional encoding derived from the land area covered in the

image. This informs the ViT of both the position and scale of

the input image. Scale-MAE also uses a Laplacian-pyramid

decoder to encourage the network to learn multiscale rep-

resentations. The embeddings are decoded to two images

containing low and residual high frequency information, re-

spectively – see Figure 2. As we discuss in Section 3, this

structure allows the ViT decoder to use fewer parameters

than MAE while still producing strong representations across

multiple scales.

We show that Scale-MAE leads to better performing,

more robust multiscale representations than both a stan-

dard MAE and a recently proposed, state-of-the-art MAEs

SatMAE [13] and ConvMAE [21] across remote sensing

datasets with a variety of scale and resolution characteristics.

To the best of our knowledge Scale-MAE is the first self-supervised MAE to include scale-aware positional encoding

and Laplacian pyramids. In our experiments, Scale-MAE

achieves an average of a 5.6%nonparametric kNN classifica-

tion improvement across eight remote sensing datasets com-

pared to current state-of-the-art in addition to a 0.9mIoU

to1.7mIoU improvement on the SpaceNet building seg-

mentation transfer task for a range of evaluation scales (see

Figure 1).

2. Related Work

Representation learning and the Masked Autoencoder.

Representation learning aims to extract meaningful, intrin-

sic features from data for downstream use [5]. In prac-

tice, this often entails pretraining a deep network so that

a lightweight learning routine can then finetune it for a par-

ticular downstream task, see [15,16,17,24,27,30,37,49,66].

The Masked Autoencoder (MAE) is a recent state-of-the-art

self-supervised representation learning method in computer

vision that pretrains a ViT encoder by masking an image,

feeding the unmasked portion into a transformer-based en-

coder, and then tasking the decoder with reconstructing the

input image [26]. MAEs fail to leverage scale information in

scale-dependent domains as they are often reliant on absolute

or relative positional encodings. To the best of our knowl-

edge, Scale-MAE is the first MAE-based self-supervised

learning method to incorporate a scale-variant positional

encoding.

Remote Sensing Representation Learning Neumann et

al. [46] were one of the first to exhaustively share results on

existing representation learning and semi-supervised learn-

ing techniques for remote sensing imagery. Gao et al. [22]

demonstrated the effectiveness of MAE pretraining for re-

mote sensing image classification. Ayush et al. [3] lever-

aged the metadata from remote sensing images via spatially

aligned but temporally separated images as positive pairs

for contrastive learning and predicted the latitude and longi-

tude as pretext tasks. Gupta et al. [25] demonstrated the use