3,757 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
On Small Satellites for Oceanography: A Survey
The recent explosive growth of small satellite operations driven primarily
from an academic or pedagogical need, has demonstrated the viability of
commercial-off-the-shelf technologies in space. They have also leveraged and
shown the need for development of compatible sensors primarily aimed for Earth
observation tasks including monitoring terrestrial domains, communications and
engineering tests. However, one domain that these platforms have not yet made
substantial inroads into, is in the ocean sciences. Remote sensing has long
been within the repertoire of tools for oceanographers to study dynamic large
scale physical phenomena, such as gyres and fronts, bio-geochemical process
transport, primary productivity and process studies in the coastal ocean. We
argue that the time has come for micro and nano satellites (with mass smaller
than 100 kg and 2 to 3 year development times) designed, built, tested and
flown by academic departments, for coordinated observations with robotic assets
in situ. We do so primarily by surveying SmallSat missions oriented towards
ocean observations in the recent past, and in doing so, we update the current
knowledge about what is feasible in the rapidly evolving field of platforms and
sensors for this domain. We conclude by proposing a set of candidate ocean
observing missions with an emphasis on radar-based observations, with a focus
on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
Remote sensing object detection (RSOD), one of the most fundamental and
challenging tasks in the remote sensing field, has received longstanding
attention. In recent years, deep learning techniques have demonstrated robust
feature representation capabilities and led to a big leap in the development of
RSOD techniques. In this era of rapid technical evolution, this review aims to
present a comprehensive review of the recent achievements in deep learning
based RSOD methods. More than 300 papers are covered in this review. We
identify five main challenges in RSOD, including multi-scale object detection,
rotated object detection, weak object detection, tiny object detection, and
object detection with limited supervision, and systematically review the
corresponding methods developed in a hierarchical division manner. We also
review the widely used benchmark datasets and evaluation metrics within the
field of RSOD, as well as the application scenarios for RSOD. Future research
directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than
300 papers relevant to the RSOD filed were reviewed in this surve
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task.Comment: 10 pages, 5 figures, accepted by CVPR 201
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
AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network
Recently, methods based on deep learning have been successfully applied to
ship detection for synthetic aperture radar (SAR) images. Despite the
development of numerous ship detection methodologies, detecting small and
coastal ships remains a significant challenge due to the limited features and
clutter in coastal environments. For that, a novel adaptive multi-hierarchical
attention module (AMAM) is proposed to learn multi-scale features and
adaptively aggregate salient features from various feature layers, even in
complex environments. Specifically, we first fuse information from adjacent
feature layers to enhance the detection of smaller targets, thereby achieving
multi-scale feature enhancement. Then, to filter out the adverse effects of
complex backgrounds, we dissect the previously fused multi-level features on
the channel, individually excavate the salient regions, and adaptively
amalgamate features originating from different channels. Thirdly, we present a
novel adaptive multi-hierarchical attention network (AMANet) by embedding the
AMAM between the backbone network and the feature pyramid network (FPN).
Besides, the AMAM can be readily inserted between different frameworks to
improve object detection. Lastly, extensive experiments on two large-scale SAR
ship detection datasets demonstrate that our AMANet method is superior to
state-of-the-art methods.Comment: 11 pages, 7 figure
Image Simulation in Remote Sensing
Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers
Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge
In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge
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