368 research outputs found
Reconnaissance de formes sur des images comprimées
Le but de ce travail est de montrer qu'il est possible de mettre en oeuvre des algorithmes de reconnaissance des formes sur des images comprimées, sans les reconstruire. La méthode de compression choisie procède par extraction des contours multiéchelles quinconce des images. Le problème de reconnaissance traité consiste à localiser des bâtiments sur des images aériennes de grande taille. Les appariements se fondent sur une distance de Hausdorff censurée entre contours, et la recherche d'un bâtiment se fait de façon hiérarchique, en commençant aux échelles grossières, pour se conclure aux échelles fines. Cette méthode est rapide sur station de travail classique, et permet d'atteindre le temps réel sur calculateur parallèle, dans un cadre opérationnel réaliste. La robustesse de la reconnaissance a été soigneusement étudiée
A Global Human Settlement Layer from optical high resolution imagery - Concept and first results
A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
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
Deep learning-based change detection in remote sensing images:a review
Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods
Deep learning for remote sensing image classification:A survey
Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel?wise and scene?wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL?based RS methods is also provided. Finally, the challenges and potential directions for further research are discussedpublishersversionPeer reviewe
Long-Range Correlation Supervision for Land-Cover Classification from Remote Sensing Images
Long-range dependency modeling has been widely considered in modern deep
learning based semantic segmentation methods, especially those designed for
large-size remote sensing images, to compensate the intrinsic locality of
standard convolutions. However, in previous studies, the long-range dependency,
modeled with an attention mechanism or transformer model, has been based on
unsupervised learning, instead of explicit supervision from the objective
ground truth. In this paper, we propose a novel supervised long-range
correlation method for land-cover classification, called the supervised
long-range correlation network (SLCNet), which is shown to be superior to the
currently used unsupervised strategies. In SLCNet, pixels sharing the same
category are considered highly correlated and those having different categories
are less relevant, which can be easily supervised by the category consistency
information available in the ground truth semantic segmentation map. Under such
supervision, the recalibrated features are more consistent for pixels of the
same category and more discriminative for pixels of other categories,
regardless of their proximity. To complement the detailed information lacking
in the global long-range correlation, we introduce an auxiliary adaptive
receptive field feature extraction module, parallel to the long-range
correlation module in the encoder, to capture finely detailed feature
representations for multi-size objects in multi-scale remote sensing images. In
addition, we apply multi-scale side-output supervision and a hybrid loss
function as local and global constraints to further boost the segmentation
accuracy. Experiments were conducted on three remote sensing datasets. Compared
with the advanced segmentation methods from the computer vision, medicine, and
remote sensing communities, the SLCNet achieved a state-of-the-art performance
on all the datasets.Comment: 14 pages, 11 figure
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