Datasets:
gremlin97
/
RemoteSensingCorpus

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
820
512-d
2x2T, 512
2x2T, 512x N timesLaplacian Block
Upsampling BlockFigure 7. (top) The Laplacian Block (LB) is a fully convolutional architecture consists of a chain of Feature Mapping Block followed by
one final Reconstruction Block. (bottom) The UpSampling Block (UB) consists of a series of transpose convolution layers separated by
LayerNorm and GELU activation.
B.2. Upsampling Block
Upsampling Blocks are used to upsample the feature map to a higher resolution. It consists of a series of 2x2 transpose
convolution layers with LayerNorm and GELU activation between them. The number of such transposed convolution layers
are a function of the output and input resolution. This is a progressive process in which we repetitively upsample the feature
map by a factor of 2 until we reach the desired target resolution. Figure 7 illustrates the architecture of these two blocks.
C. Evaluation Details
As discussed in the main experimental section, we investigated the quality of representations learned from Scale-MAE
pretraining through a set of experiments that explore their robustness to scale as well as their transfer performance to additional
tasks. We provide more information and details on these evaluations here. In order to compare with SatMAE [13] and
ConvMAE [21], for our main experiments, we pretrained Scale-MAE with a ViT-Large model using the Functional Map of
the World (FMoW) RGB training set, which consists of 363.6k images of varying image resolution and GSD. The initial
higher resolution image Ihris taken as a random 448px2crop of the input image, and the input image Iis then a downsampled
224px2fromIhr. The low frequency groundtruth is obtained by downscaling Ihrto 14px2and then upscaling to 224px2, while
the high frequency groundtruth is obtained by downscaling Ihrto 56px2and then upscaling to 448px2and subtracting this
image from Ihr. This is a common method for band pass filtering used in several super resolution works, where a high to low
to high resolution interpolation is used to obtain only low frequency results, and then high frequency results are obtained by
subtracting the low frequency image.
As further discussed in the main experimental section, we evaluate the quality of representations from Scale-MAE by
freezing the encoder and performing a nonparametric k-nearest-neighbor (kNN) classification with eight different remote
sensing imagery classification datasets with different GSDs, none of which were encountered during pretraining. All kNN
evaluations were conducted on 4 GPUs. Results are in Table 11. The kNN classifier operates by encoding all train and
validation instances, where each embedded instance in the validation set computes the cosine distance with each embedded
instance in the training set, where the instance is classified correctly if the majority of its k-nearest-neighbors are in the same
class as the validation instance. The justification for a kNN classifier evaluation is that a strong pretrained network will output
semantically grouped representation for unseen data of the same class. This evaluation for the quality of representations occurs
Scale-MAE
Ground Truth Vanilla MAE
Correct Incorrect
Scale-MAE Ground Truth Vanilla MAE
Figure 8. Visualization of Segmentation Results on SpaceNet. The left, center, right columns are ground truth labels, Scale-MAE and
vanilla MAE, respectively. The top row shows a 0.3m GSD image and the bottom row shows a 3.0m GSD image. As shown in the figure,
Scale-MAE performs better at both higher and lower GSDs.
in other notable works [7, 9, 57].
D. Visualization of SpaceNet Segmentation
Figure 8 shows an additional set of segmentation examples comparing Scale-MAE and vanilla MAE pre-trained on FMoW
and finetuned on SpaceNet v1. The left, center, right columns are ground truth labels, Scale-MAE and vanilla MAE respectively.
The top row shows a 0.3m GSD image and the bottom row shows a 3.0m GSD image. As shown in the figure, Scale-MAE
performs better at both higher and lower GSDs.
E. Glossary
E.1. Ground sample distance
Ground sample distance (GSD) is the distance between the center of one pixel to the center of an adjacent pixel in a remote
sensing image. GSD is a function of sensor parameters (such as its dimensions and focal length), image parameters (the
target dimensions of the formed image), and the geometry of the sensor with respect to the object being imaged on the Earth.
Remote sensing platforms frequently have multiple sensors to capture different wavelengths of light. Each of these sensors
have varying parameters, resulting in different GSDs for an image of the same area. Additionally, the ground is not a uniform
surface with changes in elevation common across the swath of the sensor. In total, a remote sensing platform has a sense of
absolute scale that varies along two dimensions: (1) spectrally depending on the sensor used to capture light, and (2) spatially
depending on surface elevation.
Domain Adaptation
for the Classification
of Remote Sensing Data
An overview of recent advances
DEVIS TUIA, CLAUDIO PERSELLO,
AND LORENZO BRUZZONEAdvances in Machine Learning for Remote Sensing and Geosciences
image licensed by ingram publishing
jUNE 2016 ieee Geoscience and remote sensin G ma Gazine 0274-6638/16©2016IEEE 41 The success of the supervised classification of remotely
sensed images acquired over large geographical areas or
at short time intervals strongly depends on the representa -
tivity of the samples used to train the classification algo -
rithm and to define the model. When training samples are
collected from an image or a spatial region that is different
from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such
shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the
object observed. To design classification methods that are
robust to data set shifts, recent remote sensing literature has
considered solutions based on domain adaptation (DA) approaches. Inspired by machine-learning literature, several
DA methods have been proposed to solve specific problems
in remote sensing data classification. This article provides a
critical review of the recent advances in DA approaches for
remote sensing and presents an overview of DA methods
divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and
4) selective sampling. We provide an overview of recent
Digital Object Identifier 10.1 109/MGRS.2016.2548504
Date of publication: 13 June 2016
Authorized licensed use limited to: ASU Library. Downloaded on March 08,2024 at 03:13:37 UTC from IEEE Xplore. Restrictions apply.
ieee Geoscience and remote sensin G ma Gazine june 201642
methodologies, examples of applications of the considered
techniques to real remote sensing images characterized by
very high spatial and spectral resolution as well as possible
guidelines for the selection of the method to use in real ap -
plication scenarios.
Remote Sen Sing Facing n ew o ppoRtunitie S
With the advent of the new generation of satellite mis -
sions, which are often made up of constellations of satel -
lites with short revisit time
and very high-resolution sen -
sors, the amount of remote sensing images available has increased significantly. Now -
adays, the monitoring of dy-namic processes has become possible [ 1], [2 ], and biophy-
sical parameter estimation and classification problems can be addressed with the
use of several data sources
[3]–[6 ]. As a consequence,
analysts have the opportuni -
ty to use multitemporal and multisource images for tasks such as repetitive monitoring
of the territory, change de -