[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 …
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 …
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 …
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 …
Cross-scene joint classification of multisource data with multilevel domain adaption network
Domain adaption (DA) is a challenging task that integrates knowledge from source domain
(SD) to perform data analysis for target domain. Most of the existing DA approaches only …
(SD) to perform data analysis for target domain. Most of the existing DA approaches only …
Information fusion for classification of hyperspectral and LiDAR data using IP-CNN
Joint use of multisensor information has attracted considerable attention in the remote
sensing community. While applications in land-cover observation benefit from information …
sensing community. While applications in land-cover observation benefit from information …
Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data
Hyperspectral image (HSI) provides rich spectral–spatial information and the light detection
and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for …
and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for …