[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review

J Li, D Hong, L Gao, J Yao, K Zheng, B Zhang… - International Journal of …, 2022‏ - Elsevier
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

A comparative review on multi-modal sensors fusion based on deep learning

Q Tang, J Liang, F Zhu - Signal Processing, 2023‏ - Elsevier
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 …

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

D Hong, B Zhang, H Li, Y Li, J Yao, C Li… - Remote Sensing of …, 2023‏ - Elsevier
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
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

J Yao, B Zhang, C Li, D Hong… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
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 …

Multimodal fusion transformer for remote sensing image classification

SK Roy, A Deria, D Hong, B Rasti… - … on Geoscience and …, 2023‏ - ieeexplore.ieee.org
Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …

Convolutional neural networks for multimodal remote sensing data classification

X Wu, D Hong, J Chanussot - IEEE Transactions on Geoscience …, 2021‏ - ieeexplore.ieee.org
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 …

A multilevel multimodal fusion transformer for remote sensing semantic segmentation

X Ma, X Zhang, MO Pun, M Liu - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Accurate semantic segmentation of remote sensing data plays a crucial role in the success
of geoscience research and applications. Recently, multimodal fusion-based segmentation …

Deep multimodal data fusion

F Zhao, C Zhang, B Geng - ACM computing surveys, 2024‏ - dl.acm.org
Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data
(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

G Zhao, Q Ye, L Sun, Z Wu, C Pan… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
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 …

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 …