13,269 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
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode
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
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local
climate zone (LCZ) system provides essential general information for any
studies related to urban environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
In remote sensing images, the absolute orientation of objects is arbitrary.
Depending on an object's orientation and on a sensor's flight path, objects of
the same semantic class can be observed in different orientations in the same
image. Equivariance to rotation, in this context understood as responding with
a rotated semantic label map when subject to a rotation of the input image, is
therefore a very desirable feature, in particular for high capacity models,
such as Convolutional Neural Networks (CNNs). If rotation equivariance is
encoded in the network, the model is confronted with a simpler task and does
not need to learn specific (and redundant) weights to address rotated versions
of the same object class. In this work we propose a CNN architecture called
Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation
equivariance in the network itself. By using rotating convolutions as building
blocks and passing only the the values corresponding to the maximally
activating orientation throughout the network in the form of orientation
encoding vector fields, RotEqNet treats rotated versions of the same object
with the same filter bank and therefore achieves state-of-the-art performances
even when using very small architectures trained from scratch. We test RotEqNet
in two challenging sub-decimeter resolution semantic labeling problems, and
show that we can perform better than a standard CNN while requiring one order
of magnitude less parameters
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