[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
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 …
Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
More diverse means better: Multimodal deep learning meets remote-sensing imagery classification
Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …
have long been a fundamental but challenging research topic in geoscience and remote …
Graph convolutional networks for hyperspectral image classification
Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …
Convolutional neural networks for multimodal remote sensing data classification
In recent years, enormous research has been made to improve the classification
performance of single-modal remote sensing (RS) data. However, with the ever-growing …
performance of single-modal remote sensing (RS) data. However, with the ever-growing …
Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models
The ability to quickly learn a new task with minimal instruction-known as few-shot learning-is
a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot …
a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot …
Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review
Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …
images, we have witnessed a growing interest of the remote sensing community in …
Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Interpretable hyperspectral artificial intelligence: When nonconvex modeling meets hyperspectral remote sensing
Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …