4,735 research outputs found
NeMO-Net The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment
We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%
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
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
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small and crowded in remote sensing imagery. To tackle with
this challenging task we have proposed a novel architecture called local
feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation
factors fails to aggregate local features due to sparsity of the kernel, and
detrimental to small objects. The proposed LFE module solves this problem by
aggregating local features with decreasing dilation factor. We tested our
network on three remote sensing datasets and acquired remarkably good results
for all datasets especially for small objects
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