16,669 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
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
Deep Interactive Region Segmentation and Captioning
With recent innovations in dense image captioning, it is now possible to
describe every object of the scene with a caption while objects are determined
by bounding boxes. However, interpretation of such an output is not trivial due
to the existence of many overlapping bounding boxes. Furthermore, in current
captioning frameworks, the user is not able to involve personal preferences to
exclude out of interest areas. In this paper, we propose a novel hybrid deep
learning architecture for interactive region segmentation and captioning where
the user is able to specify an arbitrary region of the image that should be
processed. To this end, a dedicated Fully Convolutional Network (FCN) named
Lyncean FCN (LFCN) is trained using our special training data to isolate the
User Intention Region (UIR) as the output of an efficient segmentation. In
parallel, a dense image captioning model is utilized to provide a wide variety
of captions for that region. Then, the UIR will be explained with the caption
of the best match bounding box. To the best of our knowledge, this is the first
work that provides such a comprehensive output. Our experiments show the
superiority of the proposed approach over state-of-the-art interactive
segmentation methods on several well-known datasets. In addition, replacement
of the bounding boxes with the result of the interactive segmentation leads to
a better understanding of the dense image captioning output as well as accuracy
enhancement for the object detection in terms of Intersection over Union (IoU).Comment: 17, pages, 9 figure
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