1,304 research outputs found
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
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
The University Defence Research Collaboration In Signal Processing
This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations.
The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
コンプレッシブ・センシングによる合成開口レーダの高分解能化
Tohoku University佐藤源之課
The University Defence Research Collaboration In Signal Processing: 2013-2018
Signal processing is an enabling technology crucial to all areas
of defence and security. It is called for whenever humans and
autonomous systems are required to interpret data (i.e. the signal)
output from sensors. This leads to the production of the
intelligence on which military outcomes depend. Signal processing
should be timely, accurate and suited to the decisions
to be made. When performed well it is critical, battle-winning
and probably the most important weapon which you’ve never
heard of.
With the plethora of sensors and data sources that are
emerging in the future network-enabled battlespace, sensing
is becoming ubiquitous. This makes signal processing more
complicated but also brings great opportunities.
The second phase of the University Defence Research Collaboration
in Signal Processing was set up to meet these complex
problems head-on while taking advantage of the opportunities.
Its unique structure combines two multi-disciplinary
academic consortia, in which many researchers can approach
different aspects of a problem, with baked-in industrial collaboration
enabling early commercial exploitation.
This phase of the UDRC will have been running for 5 years
by the time it completes in March 2018, with remarkable results.
This book aims to present those accomplishments and
advances in a style accessible to stakeholders, collaborators and
exploiters
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