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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 …
Cnns in land cover map** with remote sensing imagery: A review and meta-analysis
Convolutional neural network (CNN) comprises the most common and extensively used
network in the field of deep learning (DL). The design of CNNs was influenced by neurons …
network in the field of deep learning (DL). The design of CNNs was influenced by neurons …
Global–local transformer network for HSI and LiDAR data joint classification
Hyperspectral images (HSIs) contain rich spatial and spectral detail information, while light
detection and ranging (LiDAR) data can provide the elevation information. Thus, the fusion …
detection and ranging (LiDAR) data can provide the elevation information. Thus, the fusion …
A multistage information complementary fusion network based on flexible-mixup for HSI-X image classification
Mixup-based data augmentation has been proven to be beneficial to the regularization of
models during training, especially in the remote-sensing field where the training data is …
models during training, especially in the remote-sensing field where the training data is …
MATNet: A combining multi-attention and transformer network for hyperspectral image classification
Hyperspectral image (HSI) has rich spatial–spectral information, high spectral correlation,
and large redundancy between information. Due to the sparse background distribution of …
and large redundancy between information. Due to the sparse background distribution of …
Cross hyperspectral and LiDAR attention transformer: An extended self-attention for land use and land cover classification
The successes of attention-driven deep models like the vision transformer (ViT) have
sparked interest in cross-domain exploration. However, current transformer-based …
sparked interest in cross-domain exploration. However, current transformer-based …
ResMorCNN model: hyperspectral images classification using residual-injection morphological features and 3DCNN layers
Hyperspectral imagery is widely used for analyzing substances and objects, specifically
focusing on their classification. The advancement of processing capabilities and the …
focusing on their classification. The advancement of processing capabilities and the …
DSHFNet: Dynamic scale hierarchical fusion network based on multiattention for hyperspectral image and LiDAR data classification
Y Feng, L Song, L Wang, X Wang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the continuous improvement of satellite sensor performance, it is becoming easier to
obtain different types of remote sensing (RS) data from multiple sensors, and the fusion of …
obtain different types of remote sensing (RS) data from multiple sensors, and the fusion of …
CNN‐Transformer for visual‐tactile fusion applied in road recognition of autonomous vehicles
Reliable autonomous driving requires comprehensive environment perception, among
which the road recognition is critical for autonomous vehicles to achieve adaptability …
which the road recognition is critical for autonomous vehicles to achieve adaptability …
[HTML][HTML] Dimensionality reduction and classification of hyperspectral remote sensing image feature extraction
H Li, J Cui, X Zhang, Y Han, L Cao - Remote Sensing, 2022 - mdpi.com
Terrain classification is an important research direction in the field of remote sensing.
Hyperspectral remote sensing image data contain a large amount of rich ground object …
Hyperspectral remote sensing image data contain a large amount of rich ground object …