757 research outputs found
Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks
Remote sensing is extensively used in cartography. As transportation networks
grow and change, extracting roads automatically from satellite images is
crucial to keep maps up-to-date. Synthetic Aperture Radar satellites can
provide high resolution topographical maps. However roads are difficult to
identify in these data as they look visually similar to targets such as rivers
and railways. Most road extraction methods on Synthetic Aperture Radar images
still rely on a prior segmentation performed by classical computer vision
algorithms. Few works study the potential of deep learning techniques, despite
their successful applications to optical imagery. This letter presents an
evaluation of Fully-Convolutional Neural Networks for road segmentation in SAR
images. We study the relative performance of early and state-of-the-art
networks after carefully enhancing their sensitivity towards thin objects by
adding spatial tolerance rules. Our models shows promising results,
successfully extracting most of the roads in our test dataset. This shows that,
although Fully-Convolutional Neural Networks natively lack efficiency for road
segmentation, they are capable of good results if properly tuned. As the
segmentation quality does not scale well with the increasing depth of the
networks, the design of specialized architectures for roads extraction should
yield better performances.Comment: 5 pages, accepted for publication in IEEE Geoscience and Remote
Sensing Letter
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
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
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
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
HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data
Synthetic aperture radar (SAR) data is becoming increasingly available to a
wide range of users through commercial service providers with resolutions
reaching 0.5m/px. Segmenting SAR data still requires skilled personnel,
limiting the potential for large-scale use. We show that it is possible to
automatically and reliably perform urban scene segmentation from next-gen
resolution SAR data (0.15m/px) using deep neural networks (DNNs), achieving a
pixel accuracy of 95.19% and a mean IoU of 74.67% with data collected over a
region of merely 2.2km. The presented DNN is not only effective, but is
very small with only 63k parameters and computationally simple enough to
achieve a throughput of around 500Mpx/s using a single GPU. We further identify
that additional SAR receive antennas and data from multiple flights massively
improve the segmentation accuracy. We describe a procedure for generating a
high-quality segmentation ground truth from multiple inaccurate building and
road annotations, which has been crucial to achieving these segmentation
results
- …