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 …
[HTML][HTML] NCGLF2: Network combining global and local features for fusion of multisource remote sensing data
The fusion of multisource remote sensing (RS) data has demonstrated significant potential in
target recognition and classification tasks. However, there is limited emphasis on capturing …
target recognition and classification tasks. However, there is limited emphasis on capturing …
SS-MAE: Spatial–spectral masked autoencoder for multisource remote sensing image classification
Masked image modeling (MIM) is a highly popular and effective self-supervised learning
method for image understanding. The existing MIM-based methods mostly focus on spatial …
method for image understanding. The existing MIM-based methods mostly focus on spatial …
Hashing-based deep metric learning for the classification of hyperspectral and LiDAR data
Multisource remote sensing data provide abundant and complementary information for land
cover classification. Existing classification methods mainly focus on designing a multistream …
cover classification. Existing classification methods mainly focus on designing a multistream …
Multiattention joint convolution feature representation with lightweight transformer for hyperspectral image classification
Hyperspectral image (HSI) classification is currently a hot topic in the field of remote sensing.
The goal is to utilize the spectral and spatial information from HSI to accurately identify land …
The goal is to utilize the spectral and spatial information from HSI to accurately identify land …
Joint contextual representation model-informed interpretable network with dictionary aligning for hyperspectral and LiDAR classification
The effective utilization of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data is essential for land cover classification. Recently, deep learning-based …
(LiDAR) data is essential for land cover classification. Recently, deep learning-based …
Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults
Rolling bearings are the core components of rotating machinery, and their normal operation
is crucial to entire industrial applications. Most existing condition monitoring methods have …
is crucial to entire industrial applications. Most existing condition monitoring methods have …
Mixing self-attention and convolution: A unified framework for multi-source remote sensing data classification
Convolution and self-attention are two powerful techniques for multisource remote sensing
(RS) data fusion that have been widely adopted in Earth observation tasks. However …
(RS) data fusion that have been widely adopted in Earth observation tasks. However …
AMSSE-Net: Adaptive multiscale spatial–spectral enhancement network for classification of hyperspectral and LiDAR data
With the abundant emergence of remote sensing (RS) data sources, multimodal remote
sensing observation has become an active field. Extracting valuable information from …
sensing observation has become an active field. Extracting valuable information from …
Multiscale 3-d–2-d mixed cnn and lightweight attention-free transformer for hyperspectral and lidar classification
The effective combination of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data can be used for land cover classification. Recently, deep-learning-based …
(LiDAR) data can be used for land cover classification. Recently, deep-learning-based …