Recent advances on spectral–spatial hyperspectral image classification: An overview and new guidelines
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …
last four decades from being a sparse research tool into a commodity product available to a …
Hyperspectral image classification with multi-attention transformer and adaptive superpixel segmentation-based active learning
Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
Super-resolution map** based on spatial–spectral correlation for spectral imagery
P Wang, L Wang, H Leung… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Due to the influences of imaging conditions, spectral imagery can be coarse and contain a
large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover …
large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover …
Learning compact and discriminative stacked autoencoder for hyperspectral image classification
As one of the fundamental research topics in remote sensing image analysis, hyperspectral
image (HSI) classification has been extensively studied so far. However, how to …
image (HSI) classification has been extensively studied so far. However, how to …
Hyperspectral anomaly detection with attribute and edge-preserving filters
A novel method for anomaly detection in hyperspectral images is proposed. The method is
based on two ideas. First, compared with the surrounding background, objects with …
based on two ideas. First, compared with the surrounding background, objects with …
PCA-based edge-preserving features for hyperspectral image classification
Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …
hyperspectral images (HSIs) have been found very effective in characterizing significant …
Joint classification of hyperspectral and LiDAR data using hierarchical random walk and deep CNN architecture
Earth observation using multisensor data is drawing increasing attention. Fusing remotely
sensed hyperspectral imagery and light detection and ranging (LiDAR) data helps to …
sensed hyperspectral imagery and light detection and ranging (LiDAR) data helps to …
Infrared small target detection based on facet kernel and random walker
Efficient detection of targets immersed in a complex background with a low signal-to-clutter
ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this …
ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this …
Diversity-connected graph convolutional network for hyperspectral image classification
Y Ding, Y Chong, S Pan… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Hyperspectral image (HSI) classification methods based on the graph convolutional network
(GCN) have received more attention because they can handle irregular regions by graph …
(GCN) have received more attention because they can handle irregular regions by graph …
Fractional Gabor convolutional network for multisource remote sensing data classification
Remote sensing using multisensor platforms has been systematically applied for monitoring
and optimizing human activities. Several advanced techniques have been developed to …
and optimizing human activities. Several advanced techniques have been developed to …