6,972 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition
This study explores the application of self-supervised learning (SSL) for
improved target recognition in synthetic aperture sonar (SAS) imagery. The
unique challenges of underwater environments make traditional computer vision
techniques, which rely heavily on optical camera imagery, less effective. SAS,
with its ability to generate high-resolution imagery, emerges as a preferred
choice for underwater imaging. However, the voluminous high-resolution SAS data
presents a significant challenge for labeling; a crucial step for training deep
neural networks (DNNs).
SSL, which enables models to learn features in data without the need for
labels, is proposed as a potential solution to the data labeling challenge in
SAS. The study evaluates the performance of two prominent SSL algorithms,
MoCov2 and BYOL, against the well-regarded supervised learning model, ResNet18,
for binary image classification tasks. The findings suggest that while both SSL
models can outperform a fully supervised model with access to a small number of
labels in a few-shot scenario, they do not exceed it when all the labels are
used.
The results underscore the potential of SSL as a viable alternative to
traditional supervised learning, capable of maintaining task performance while
reducing the time and costs associated with data labeling. The study also
contributes to the growing body of evidence supporting the use of SSL in remote
sensing and could stimulate further research in this area
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