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
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
Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery
In this paper we discuss the potential and challenges regarding SAR-optical
stereogrammetry for urban areas, using very-high-resolution (VHR) remote
sensing imagery. Since we do this mainly from a geometrical point of view, we
first analyze the height reconstruction accuracy to be expected for different
stereogrammetric configurations. Then, we propose a strategy for simultaneous
tie point matching and 3D reconstruction, which exploits an epipolar-like
search window constraint. To drive the matching and ensure some robustness, we
combine different established handcrafted similarity measures. For the
experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and
MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR
imagery is generally feasible with 3D positioning accuracies in the
meter-domain, although the matching of these strongly hetereogeneous
multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar
(SAR), optical images, remote sensing, data fusion, stereogrammetr
Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks
This paper addresses the highly challenging problem of automatically
detecting man-made structures especially buildings in very high resolution
(VHR) synthetic aperture radar (SAR) images. In this context, the paper has two
major contributions: Firstly, it presents a novel and generic workflow that
initially classifies the spaceborne TomoSAR point clouds generated by
processing VHR SAR image stacks using advanced interferometric techniques known
as SAR tomography (TomoSAR) into buildings and non-buildings with the aid
of auxiliary information (i.e., either using openly available 2-D building
footprints or adopting an optical image classification scheme) and later back
project the extracted building points onto the SAR imaging coordinates to
produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR
datasets. Secondly, these labelled datasets (i.e., building masks) have been
utilized to construct and train the state-of-the-art deep Fully Convolution
Neural Networks with an additional Conditional Random Field represented as a
Recurrent Neural Network to detect building regions in a single VHR SAR image.
Such a cascaded formation has been successfully employed in computer vision and
remote sensing fields for optical image classification but, to our knowledge,
has not been applied to SAR images. The results of the building detection are
illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering
approximately 39 km almost the whole city of Berlin with mean
pixel accuracies of around 93.84%Comment: Accepted publication in IEEE TGR
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
DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers
The detection of flooded areas using high-resolution synthetic aperture radar
(SAR) imagery is a critical task with applications in crisis and disaster
management, as well as environmental resource planning. However, the complex
nature of SAR images presents a challenge that often leads to an overestimation
of the flood extent. To address this issue, we propose a novel differential
attention metric-based network (DAM-Net) in this study. The DAM-Net comprises
two key components: a weight-sharing Siamese backbone to obtain multi-scale
change features of multi-temporal images and tokens containing high-level
semantic information of water-body changes, and a temporal differential fusion
(TDF) module that integrates semantic tokens and change features to generate
flood maps with reduced speckle noise. Specifically, the backbone is split into
multiple stages. In each stage, we design three modules, namely, temporal-wise
feature extraction (TWFE), cross-temporal change attention (CTCA), and
temporal-aware change enhancement (TACE), to effectively extract the change
features. In TACE of the last stage, we introduce a class token to record
high-level semantic information of water-body changes via the attention
mechanism. Another challenge faced by data-driven deep learning algorithms is
the limited availability of flood detection datasets. To overcome this, we have
created the S1GFloods open-source dataset, a global-scale high-resolution
Sentinel-1 SAR image pairs dataset covering 46 global flood events between 2015
and 2022. The experiments on the S1GFloods dataset using the proposed DAM-Net
showed top results compared to state-of-the-art methods in terms of overall
accuracy, F1-score, and IoU, which reached 97.8%, 96.5%, and 93.2%,
respectively. Our dataset and code will be available online at
https://github.com/Tamer-Saleh/S1GFlood-Detection.Comment: 16 pages, 11 figure
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode
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