3,012 research outputs found
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
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently trains a neural model as a committee machine of subnetworks, each
capable of predicting with a subset of the original input features. We discuss
the application of the DropIn methodology in the context of Reservoir Computing
models and targeting applications characterized by input sources that are
unreliable or prone to be disconnected, such as in pervasive wireless sensor
networks and ambient intelligence. We provide an experimental assessment using
real-world data from such application domains, showing how the Dropin
methodology allows to maintain predictive performances comparable to those of a
model without missing features, even when 20\%-50\% of the inputs are not
available
Deep Transductive Transfer Learning for Automatic Target Recognition
One of the major obstacles in designing an automatic target recognition (ATR)
algorithm, is that there are often labeled images in one domain (i.e., infrared
source domain) but no annotated images in the other target domains (i.e.,
visible, SAR, LIDAR). Therefore, automatically annotating these images is
essential to build a robust classifier in the target domain based on the
labeled images of the source domain. Transductive transfer learning is an
effective way to adapt a network to a new target domain by utilizing a
pretrained ATR network in the source domain. We propose an unpaired
transductive transfer learning framework where a CycleGAN model and a
well-trained ATR classifier in the source domain are used to construct an ATR
classifier in the target domain without having any labeled data in the target
domain. We employ a CycleGAN model to transfer the mid-wave infrared (MWIR)
images to visible (VIS) domain images (or visible to MWIR domain). To train the
transductive CycleGAN, we optimize a cost function consisting of the
adversarial, identity, cycle-consistency, and categorical cross-entropy loss
for both the source and target classifiers. In this paper, we perform a
detailed experimental analysis on the challenging DSIAC ATR dataset. The
dataset consists of ten classes of vehicles at different poses and distances
ranging from 1-5 kilometers on both the MWIR and VIS domains. In our
experiment, we assume that the images in the VIS domain are the unlabeled
target dataset. We first detect and crop the vehicles from the raw images and
then project them into a common distance of 2 kilometers. Our proposed
transductive CycleGAN achieves 71.56% accuracy in classifying the visible
domain vehicles in the DSIAC ATR dataset.Comment: 10 pages, 5 figure
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