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[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 …
A comparative review on multi-modal sensors fusion based on deep learning
The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …
data with characteristics of high volume, wide variety, and high integrity. However, traditional …
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 …
Extended vision transformer (ExViT) for land use and land cover classification: A multimodal deep learning framework
The recent success of attention mechanism-driven deep models, like vision transformer (ViT)
as one of the most representatives, has intrigued a wave of advanced research to explore …
as one of the most representatives, has intrigued a wave of advanced research to explore …
Multimodal fusion transformer for remote sensing image classification
Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …
promising performance when compared with convolutional neural networks (CNNs). As a …
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 …
A multilevel multimodal fusion transformer for remote sensing semantic segmentation
Accurate semantic segmentation of remote sensing data plays a crucial role in the success
of geoscience research and applications. Recently, multimodal fusion-based segmentation …
of geoscience research and applications. Recently, multimodal fusion-based segmentation …
Deep multimodal data fusion
Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data
(eg, images, texts, or data collected from different sensors), feature engineering (eg …
(eg, images, texts, or data collected from different sensors), feature engineering (eg …
Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer
The joint use of multisource remote-sensing (RS) data for Earth observation missions has
drawn much attention. Although the fusion of several data sources can improve the accuracy …
drawn much attention. Although the fusion of several data sources can improve the accuracy …
Deep hierarchical vision transformer for hyperspectral and LiDAR data classification
Z Xue, X Tan, X Yu, B Liu, A Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture
for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current …
for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current …