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 …
Semi-supervised multiscale dynamic graph convolution network for hyperspectral image classification
In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …
Deep reinforcement learning for band selection in hyperspectral image classification
Band selection refers to the process of choosing the most relevant bands in a hyperspectral
image. By selecting a limited number of optimal bands, we aim at speeding up model …
image. By selecting a limited number of optimal bands, we aim at speeding up model …
A review of remote sensing image segmentation by deep learning methods
Remote sensing (RS) images enable high-resolution information collection from complex
ground objects and are increasingly utilized in the earth observation research. Recently, RS …
ground objects and are increasingly utilized in the earth observation research. Recently, RS …
A dual global–local attention network for hyperspectral band selection
K He, W Sun, G Yang, X Meng, K Ren… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This article proposes a dual global–local attention network (DGLAnet), which is an end-to-
end unsupervised band selection (UBS) method that fully utilizes spatial and spectral …
end unsupervised band selection (UBS) method that fully utilizes spatial and spectral …
Robust dual graph self-representation for unsupervised hyperspectral band selection
Unsupervised band selection aims to select informative spectral bands to preprocess
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …
Adversarial domain alignment with contrastive learning for hyperspectral image classification
F Liu, W Gao, J Liu, X Tang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recently, deep learning-based hyperspectral image (HSI) classification techniques are
flourishing and exhibit good performance, where cross-domain information is usually utilized …
flourishing and exhibit good performance, where cross-domain information is usually utilized …
Spatial–spectral transformer with cross-attention for hyperspectral image classification
Y Peng, Y Zhang, B Tu, Q Li, W Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI)
classification tasks because of their excellent local spatial feature extraction capabilities …
classification tasks because of their excellent local spatial feature extraction capabilities …
Hyperspectral band selection via region-aware latent features fusion based clustering
Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …
Self-supervised divide-and-conquer generative adversarial network for classification of hyperspectral images
Generative adversarial network (GAN) has been rapidly developed because of its powerful
generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) …
generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) …