Spectral–spatial feature tokenization transformer for hyperspectral image classification
In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …
category. In the recent past, convolutional neural network (CNN)-based HSI classification …
Hyperspectral and LiDAR data classification based on structural optimization transmission
With the development of the sensor technology, complementary data of different sources can
be easily obtained for various applications. Despite the availability of adequate multisource …
be easily obtained for various applications. Despite the availability of adequate multisource …
Hyperspectral image transformer classification networks
Hyperspectral image (HSI) classification is an important task in earth observation missions.
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …
Morphological transformation and spatial-logical aggregation for tree species classification using hyperspectral imagery
Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …
contribute to a more accurate identification of materials and land covers. However, most …
Hyperspectral and SAR image classification via multiscale interactive fusion network
Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …
sensing data has received increasing attention. However, existing methods still have certain …
Classification via structure-preserved hypergraph convolution network for hyperspectral image
Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …
learning has gained increasing attention in hyperspectral image (HSI) classification …
Lessformer: Local-enhanced spectral-spatial transformer for hyperspectral image classification
Currently, the convolutional neural networks (CNNs) have become the mainstream methods
for hyperspectral image (HSI) classification, due to their powerful ability to extract local …
for hyperspectral image (HSI) classification, due to their powerful ability to extract local …
Local semantic feature aggregation-based transformer for hyperspectral image classification
B Tu, X Liao, Q Li, Y Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Hyperspectral images (HSIs) contain abundant information in the spatial and spectral
domains, allowing for a precise characterization of categories of materials. Convolutional …
domains, allowing for a precise characterization of categories of materials. Convolutional …
Multiscale and cross-level attention learning for hyperspectral image classification
F Xu, G Zhang, C Song, H Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Transformer-based networks, which can well model the global characteristics of inputted
data using the attention mechanism, have been widely applied to hyperspectral image (HSI) …
data using the attention mechanism, have been widely applied to hyperspectral image (HSI) …
Hyperspectral image classification based on multibranch attention transformer networks
Deep learning (DL) has become a mainstream method of hyperspectral image (HSI)
classification. Many DL-based methods exploit spatial-spectral features to achieve better …
classification. Many DL-based methods exploit spatial-spectral features to achieve better …