[HTML][HTML] A review on deep learning in UAV remote sensing
Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …
capability, and brought important breakthroughs for processing images, time-series, natural …
AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture
Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …
costs and environmental footprints, and therefore is attracting ever-increasing interests in …
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] 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) …
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 …
Hyperspectral image classification with convolutional neural network and active learning
Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …
classification recently. However, its success is greatly attributed to numerous labeled …
Spectral partitioning residual network with spatial attention mechanism for hyperspectral image classification
Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …
Generative adversarial networks for hyperspectral image classification
A generative adversarial network (GAN) usually contains a generative network and a
discriminative network in competition with each other. The GAN has shown its capability in a …
discriminative network in competition with each other. The GAN has shown its capability in a …
Deep feature extraction and classification of hyperspectral images based on convolutional neural networks
Due to the advantages of deep learning, in this paper, a regularized deep feature extraction
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …
Invariant attribute profiles: A spatial-frequency joint feature extractor for hyperspectral image classification
So far, a large number of advanced techniques have been developed to enhance and
extract the spatially semantic information in hyperspectral image processing and analysis …
extract the spatially semantic information in hyperspectral image processing and analysis …