Sensors, features, and machine learning for oil spill detection and monitoring: A review
Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …
[HTML][HTML] Polarimetric imaging via deep learning: A review
Polarization can provide information largely uncorrelated with the spectrum and intensity.
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
Deep learning meets SAR: Concepts, models, pitfalls, and perspectives
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it
is important to understand how decisions are made. Fairness, and transparency becomes …
is important to understand how decisions are made. Fairness, and transparency becomes …
SAR target classification using the multikernel-size feature fusion-based convolutional neural network
It is well-known that the convolutional neural network (CNN) is an effective method for
synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN …
synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN …
A new end-to-end multi-dimensional CNN framework for land cover/land use change detection in multi-source remote sensing datasets
ST Seydi, M Hasanlou, M Amani - Remote Sensing, 2020 - mdpi.com
The diversity of change detection (CD) methods and the limitations in generalizing these
techniques using different types of remote sensing datasets over various study areas have …
techniques using different types of remote sensing datasets over various study areas have …
Synthetic Aperture Radar image analysis based on deep learning: A review of a decade of research
Artificial intelligence research in the area of computer vision teaches machines to
comprehend and interpret visual data. Machines can properly recognize and classify items …
comprehend and interpret visual data. Machines can properly recognize and classify items …
Contrastive learning-based dual dynamic GCN for SAR image scene classification
As a typical label-limited task, it is significant and valuable to explore networks that enable to
utilize labeled and unlabeled samples simultaneously for synthetic aperture radar (SAR) …
utilize labeled and unlabeled samples simultaneously for synthetic aperture radar (SAR) …
Are indices of polarimetric purity excellent metrics for object identification in scattering media?
Polarization characteristics are significantly crucial for tasks in various fields, including the
remote sensing of oceans and atmosphere, as well as the polarization LIDAR and …
remote sensing of oceans and atmosphere, as well as the polarization LIDAR and …
Deep support vector machine for PolSAR image classification
The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image
classification in remote sensing is the ability to develop classifiers that can substantially …
classification in remote sensing is the ability to develop classifiers that can substantially …