Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks
S Ghaderizadeh, D Abbasi-Moghadam… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Due to the unique feature of the three-dimensional convolution neural network, it is used in
image classification. There are some problems such as noise, lack of labeled samples, the …
image classification. There are some problems such as noise, lack of labeled samples, the …
A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification
Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …
classification and have achieved great success. However, the deep neural network model …
A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection
COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of
cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a …
cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a …
[HTML][HTML] Marine floating raft aquaculture extraction of hyperspectral remote sensing images based decision tree algorithm
T Hou, W Sun, C Chen, G Yang, X Meng… - International Journal of …, 2022 - Elsevier
The accurate extraction and mapping of floating raft aquaculture (FRA) is significant to the
scientific management and sustainable development of coastal zones. However, the current …
scientific management and sustainable development of coastal zones. However, the current …
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 …
Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …
[PDF][PDF] Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review.
S Bera, VK Shrivastava… - CMES-Computer Modeling …, 2022 - researchgate.net
Hyperspectral image (HSI) classification has been one of the most important tasks in the
remote sensing community over the last few decades. Due to the presence of highly …
remote sensing community over the last few decades. Due to the presence of highly …
Swin transformer and deep convolutional neural networks for coastal wetland classification using sentinel-1, sentinel-2, and LiDAR data
A Jamali, M Mahdianpari - Remote Sensing, 2022 - mdpi.com
The use of machine learning algorithms to classify complex landscapes has been
revolutionized by the introduction of deep learning techniques, particularly in remote …
revolutionized by the introduction of deep learning techniques, particularly in remote …
Morphological convolutional neural networks for hyperspectral image classification
Convolutional neural networks (CNNs) have become quite popular for solving many
different tasks in remote sensing data processing. The convolution is a linear operation …
different tasks in remote sensing data processing. The convolution is a linear operation …
Application of pre-trained deep convolutional neural networks for rice plant disease classification
VK Shrivastava, MK Pradhan… - … conference on artificial …, 2021 - ieeexplore.ieee.org
Rice is a primary food and encounters an essential role in providing food security worldwide.
However, several diseases affect this crop that significantly reduces its production and …
However, several diseases affect this crop that significantly reduces its production and …