Self-supervised pretraining via multimodality images with transformer for change detection
Self-supervised learning (SSL) has shown remarkable success in image representation
learning. Among these methods, masked image modeling and contrastive learning are the …
learning. Among these methods, masked image modeling and contrastive learning are the …
[HTML][HTML] A fast and robust method for detecting trend turning points in InSAR displacement time series
E Ghaderpour, B Antonielli, F Bozzano… - Computers & …, 2024 - Elsevier
Ground deformation monitoring is a crucial task in geohazard management to ensure the
safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar …
safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar …
Real-time change-point detection: A deep neural network-based adaptive approach for detecting changes in multivariate time series data
The behavior of a time series may be affected by various factors. Changes in mean,
variance, frequency, and auto-correlation are the most common. Change-Point Detection …
variance, frequency, and auto-correlation are the most common. Change-Point Detection …
Comprehensive analysis of change-point dynamics detection in time series data: A review
In the ever-evolving field of time series analysis, detecting changes in patterns and
dynamics is paramount for accurate forecasting and meaningful insights. This article …
dynamics is paramount for accurate forecasting and meaningful insights. This article …
A self-supervised contrastive change point detection method for industrial time series
X Bao, L Chen, J Zhong, D Wu, Y Zheng - Engineering Applications of …, 2024 - Elsevier
Manufacturing process monitoring is crucial to ensure production quality. This paper
formulates the detection problem of abnormal changes in the manufacturing process as the …
formulates the detection problem of abnormal changes in the manufacturing process as the …
Supervised machine learning algorithms for ground motion time series classification from InSAR data
The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the
generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of …
generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of …
Unpaired speckle extraction for SAR despeckling
Speckle suppression is a critical step in synthetic aperture radar (SAR) imaging. Since
speckle-free SAR images are inaccessible, supervised denoising methods are not suitable …
speckle-free SAR images are inaccessible, supervised denoising methods are not suitable …
Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond
In recent years, deep learning has significantly reshaped numerous fields and applications,
fundamentally altering how we tackle a variety of challenges. Areas such as natural …
fundamentally altering how we tackle a variety of challenges. Areas such as natural …
Deep learning-based landslide recognition incorporating deformation characteristics
Landslide disasters pose a significant threat, with their highly destructive nature
underscoring the critical importance of timely and accurate recognition for effective early …
underscoring the critical importance of timely and accurate recognition for effective early …