68 research outputs found
The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion
While deep learning techniques have an increasing impact on many technical
fields, gathering sufficient amounts of training data is a challenging problem
in remote sensing. In particular, this holds for applications involving data
from multiple sensors with heterogeneous characteristics. One example for that
is the fusion of synthetic aperture radar (SAR) data and optical imagery. With
this paper, we publish the SEN1-2 dataset to foster deep learning research in
SAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding image
patches, collected from across the globe and throughout all meteorological
seasons. Besides a detailed description of the dataset, we show exemplary
results for several possible applications, such as SAR image colorization,
SAR-optical image matching, and creation of artificial optical images from SAR
input data. Since SEN1-2 is the first large open dataset of this kind, we
believe it will support further developments in the field of deep learning for
remote sensing as well as multi-sensor data fusion.Comment: accepted for publication in the ISPRS Annals of the Photogrammetry,
Remote Sensing and Spatial Information Sciences (online from October 2018
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
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
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
THE SEN1-2 DATASET FOR DEEP LEARNING IN SAR-OPTICAL DATA FUSION
While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing. In particular, this holds for applications involving data from multiple sensors with heterogeneous characteristics. One example for that is the fusion of synthetic aperture radar (SAR) data and optical imagery. With this paper, we publish the SEN1-2 dataset to foster deep learning research in SAR-optical data fusion. SEN1-2 comprises 282;384 pairs of corresponding image patches, collected from across the globe and throughout all meteorological seasons. Besides a detailed description of the dataset, we show exemplary results for several possible applications, such as SAR image colorization, SAR-optical image matching, and creation of artificial optical images from SAR input data. Since SEN1-2 is the first large open dataset of this kind, we believe it will support further developments in the field of deep learning for remote sensing as well as multi-sensor data fusion
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
- …