271 research outputs found
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
Advances in Object and Activity Detection in Remote Sensing Imagery
The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms
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
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
With the recent advances of deep learning, automatic target recognition (ATR)
of synthetic aperture radar (SAR) has achieved superior performance. By not
being limited to the target category, the SAR ATR system could benefit from the
simultaneous extraction of multifarious target attributes. In this paper, we
propose a new multi-task learning approach for SAR ATR, which could obtain the
accurate category and precise shape of the targets simultaneously. By
introducing deep learning theory into multi-task learning, we first propose a
novel multi-task deep learning framework with two main structures: encoder and
decoder. The encoder is constructed to extract sufficient image features in
different scales for the decoder, while the decoder is a tasks-specific
structure which employs these extracted features adaptively and optimally to
meet the different feature demands of the recognition and segmentation.
Therefore, the proposed framework has the ability to achieve superior
recognition and segmentation performance. Based on the Moving and Stationary
Target Acquisition and Recognition (MSTAR) dataset, experimental results show
the superiority of the proposed framework in terms of recognition and
segmentation
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
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
Sufficient synthetic aperture radar (SAR) target images are very important
for the development of researches. However, available SAR target images are
often limited in practice, which hinders the progress of SAR application. In
this paper, we propose an azimuth-controllable generative adversarial network
to generate precise SAR target images with an intermediate azimuth between two
given SAR images' azimuths. This network mainly contains three parts:
generator, discriminator, and predictor. Through the proposed specific network
structure, the generator can extract and fuse the optimal target features from
two input SAR target images to generate SAR target image. Then a similarity
discriminator and an azimuth predictor are designed. The similarity
discriminator can differentiate the generated SAR target images from the real
SAR images to ensure the accuracy of the generated, while the azimuth predictor
measures the difference of azimuth between the generated and the desired to
ensure the azimuth controllability of the generated. Therefore, the proposed
network can generate precise SAR images, and their azimuths can be controlled
well by the inputs of the deep network, which can generate the target images in
different azimuths to solve the small sample problem to some degree and benefit
the researches of SAR images. Extensive experimental results show the
superiority of the proposed method in azimuth controllability and accuracy of
SAR target image generation
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