Coupling of deep learning and remote sensing: a comprehensive systematic literature review
M Yasir, W Jianhua, L Shanwei, H Sheng… - … Journal of Remote …, 2023 - Taylor & Francis
This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …
Consolidated convolutional neural network for hyperspectral image classification
The performance of hyperspectral image (HSI) classification is highly dependent on spatial
and spectral information, and is heavily affected by factors such as data redundancy and …
and spectral information, and is heavily affected by factors such as data redundancy and …
A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting
Due to the intermittency and fluctuation of solar energy, its exponential growth presents
serious challenges to the power system. Therefore, photovoltaic (PV) power forecasting …
serious challenges to the power system. Therefore, photovoltaic (PV) power forecasting …
Swin transformer with multiscale 3D atrous convolution for hyperspectral image classification
Hyperspectral image (HSI) classification has attracted significant interest among researchers
owing to its diverse practical applications. Convolutional neural networks (CNNs) have been …
owing to its diverse practical applications. Convolutional neural networks (CNNs) have been …
Multiscale feature fusion network incorporating 3D self-attention for hyperspectral image classification
Y Qing, Q Huang, L Feng, Y Qi, W Liu - Remote Sensing, 2022 - mdpi.com
In recent years, the deep learning-based hyperspectral image (HSI) classification method
has achieved great success, and the convolutional neural network (CNN) method has …
has achieved great success, and the convolutional neural network (CNN) method has …
Deep learning algorithms for hyperspectral remote sensing classifications: an applied review
M Pal - International Journal of Remote Sensing, 2024 - Taylor & Francis
Over last decade, hundreds of deep learning algorithms using CNN, ViT, MLP, and deep
LSTM are proposed to classify hyperspectral remote sensing images with accuracy reaching …
LSTM are proposed to classify hyperspectral remote sensing images with accuracy reaching …
A dual attention driven multiscale-multilevel feature fusion approach for hyperspectral image classification
Deep learning has achieved promising results for hyperspectral image (HSI) classification in
recent years due to its hierarchical structure and automatic feature extraction ability from raw …
recent years due to its hierarchical structure and automatic feature extraction ability from raw …
Spiking-LSTM: A novel hyperspectral image segmentation network for Sclerotinia detection
J Zhang, Y Zhao, J Yan, X Yin, Z Ji, H Zhang… - … and Electronics in …, 2024 - Elsevier
Sclerotinia is a worldwide disease that often occurs at all growth stages of rapeseed, and
can lead to 10%∼ 70% yield decline. It will also drastically reduce the oil content of seeds …
can lead to 10%∼ 70% yield decline. It will also drastically reduce the oil content of seeds …
Pseudo complex-valued deformable ConvLSTM neural network with mutual attention learning for hyperspectral image classification
Convolutional long short-term memory (ConvLSTM) has received much attention for
hyperspectral image (HSI) classification due to its ability of modeling long-range …
hyperspectral image (HSI) classification due to its ability of modeling long-range …
Deep convolutional transformer network for hyperspectral unmixing
Hyperspectral unmixing (HU) is considered one of the most important ways to improve
hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral …
hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral …