Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Deep learning for hyperspectral image classification: An overview
Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …
sensing. In general, the complex characteristics of hyperspectral data make the accurate …
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 …
[HTML][HTML] Deep learning classifiers for hyperspectral imaging: A review
Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification
Y Ding, Z Zhang, X Zhao, D Hong, W Cai, C Yu, N Yang… - Neurocomputing, 2022 - Elsevier
Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …
[HTML][HTML] A survey: Deep learning for hyperspectral image classification with few labeled samples
With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification
Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …
Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox
Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …
CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification
Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …
hyperspectral image (HSI) classification. Compared with the convolutional neural network …
Cascaded recurrent neural networks for hyperspectral image classification
By considering the spectral signature as a sequence, recurrent neural networks (RNNs)
have been successfully used to learn discriminative features from hyperspectral images …
have been successfully used to learn discriminative features from hyperspectral images …