Self-supervised learning in remote sensing: A review
Y Wang, CM Albrecht, NAA Braham… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …
triggering interest within both the computer vision and remote sensing communities. While …
Big Data in Earth system science and progress towards a digital twin
The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …
physics-based models in an interactive computational framework that enables monitoring …
RingMo: A remote sensing foundation model with masked image modeling
Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Remoteclip: A vision language foundation model for remote sensing
General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …
Global and local contrastive self-supervised learning for semantic segmentation of HR remote sensing images
Recently, supervised deep learning has achieved a great success in remote sensing image
(RSI) semantic segmentation. However, supervised learning for semantic segmentation …
(RSI) semantic segmentation. However, supervised learning for semantic segmentation …
Nearest neighbor-based contrastive learning for hyperspectral and LiDAR data classification
The joint hyperspectral image (HSI) and light detection and ranging (LiDAR) data
classification aims to interpret ground objects at more detailed and precise level. Although …
classification aims to interpret ground objects at more detailed and precise level. Although …
Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works
Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …
sensing images (RSIs). To better understand the connection between three feature learning …
Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs
Most supervised geographic mapping methods with very-high-resolution (VHR) images are
designed for a specific task, leading to high label-dependency and inadequate task …
designed for a specific task, leading to high label-dependency and inadequate task …
CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders
A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …