149 research outputs found
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce
best possible decisions by integrating information coming from multiple media.
While most of the past multimodal approaches either work by projecting the
features of different modalities into the same space, or by coordinating the
representations of each modality through the use of constraints, our approach
borrows from both visions. More specifically, assuming each modality can be
processed by a separated deep convolutional network, allowing to take decisions
independently from each modality, we introduce a central network linking the
modality specific networks. This central network not only provides a common
feature embedding but also regularizes the modality specific networks through
the use of multi-task learning. The proposed approach is validated on 4
different computer vision tasks on which it consistently improves the accuracy
of existing multimodal fusion approaches
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
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Ship detection has been playing a significant role in the field of remote
sensing for a long time but it is still full of challenges. The main
limitations of traditional ship detection methods usually lie in the complexity
of application scenarios, the difficulty of intensive object detection and the
redundancy of detection region. In order to solve such problems above, we
propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN)
which can effectively detect ship in different scenes including ocean and port.
Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is
aimed at solving the problem resulted from the narrow width of the ship.
Compared with previous multi-scale detectors such as Feature Pyramid Network
(FPN), DFPN builds the high-level semantic feature-maps for all scales by means
of dense connections, through which enhances the feature propagation and
encourages the feature reuse. Additionally, in the case of ship rotation and
dense arrangement, we design a rotation anchor strategy to predict the minimum
circumscribed rectangle of the object so as to reduce the redundant detection
region and improve the recall. Furthermore, we also propose multi-scale ROI
Align for the purpose of maintaining the completeness of semantic and spatial
information. Experiments based on remote sensing images from Google Earth for
ship detection show that our detection method based on R-DFPN representation
has a state-of-the-art performance.Comment: 14 pages, 11 figure
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
Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges
The deep learning, which is a dominating technique in artificial
intelligence, has completely changed the image understanding over the past
decade. As a consequence, the sea ice extraction (SIE) problem has reached a
new era. We present a comprehensive review of four important aspects of SIE,
including algorithms, datasets, applications, and the future trends. Our review
focuses on researches published from 2016 to the present, with a specific focus
on deep learning-based approaches in the last five years. We divided all
relegated algorithms into 3 categories, including classical image segmentation
approach, machine learning-based approach and deep learning-based methods. We
reviewed the accessible ice datasets including SAR-based datasets, the
optical-based datasets and others. The applications are presented in 4 aspects
including climate research, navigation, geographic information systems (GIS)
production and others. It also provides insightful observations and inspiring
future research directions.Comment: 24 pages, 6 figure
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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