15,741 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
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots
For robots that have the capability to interact with the physical environment
through their end effectors, understanding the surrounding scenes is not merely
a task of image classification or object recognition. To perform actual tasks,
it is critical for the robot to have a functional understanding of the visual
scene. Here, we address the problem of localizing and recognition of functional
areas from an arbitrary indoor scene, formulated as a two-stage deep learning
based detection pipeline. A new scene functionality testing-bed, which is
complied from two publicly available indoor scene datasets, is used for
evaluation. Our method is evaluated quantitatively on the new dataset,
demonstrating the ability to perform efficient recognition of functional areas
from arbitrary indoor scenes. We also demonstrate that our detection model can
be generalized onto novel indoor scenes by cross validating it with the images
from two different datasets
Generative Image Modeling Using Spatial LSTMs
Modeling the distribution of natural images is challenging, partly because of
strong statistical dependencies which can extend over hundreds of pixels.
Recurrent neural networks have been successful in capturing long-range
dependencies in a number of problems but only recently have found their way
into generative image models. We here introduce a recurrent image model based
on multi-dimensional long short-term memory units which are particularly suited
for image modeling due to their spatial structure. Our model scales to images
of arbitrary size and its likelihood is computationally tractable. We find that
it outperforms the state of the art in quantitative comparisons on several
image datasets and produces promising results when used for texture synthesis
and inpainting
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