SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery
As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …
(PCA) has been widely considered as an efficient and effective preprocessing step for …
Adjacent context coordination network for salient object detection in optical remote sensing images
Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an
emerging topic in understanding optical RSIs. However, due to the difference between …
emerging topic in understanding optical RSIs. However, due to the difference between …
Remote sensing image scene classification using bag of convolutional features
More recently, remote sensing image classification has been moving from pixel-level
interpretation to scene-level semantic understanding, which aims to label each scene image …
interpretation to scene-level semantic understanding, which aims to label each scene image …
[HTML][HTML] Classification for high resolution remote sensing imagery using a fully convolutional network
G Fu, C Liu, R Zhou, T Sun, Q Zhang - Remote Sensing, 2017 - mdpi.com
As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully
Convolutional Network (FCN) model achieved state-of-the-art performance for natural image …
Convolutional Network (FCN) model achieved state-of-the-art performance for natural image …
Visual attention-driven hyperspectral image classification
Deep neural networks (DNNs), including convolutional neural networks (CNNs) and
residual networks (ResNets) models, are able to learn abstract representations from the …
residual networks (ResNets) models, are able to learn abstract representations from the …
3-D quasi-recurrent neural network for hyperspectral image denoising
In this article, we propose an alternating directional 3-D quasi-recurrent neural network for
hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge …
hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge …
Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images
B Zhang, L Zhao, X Zhang - Remote Sensing of Environment, 2020 - Elsevier
Airborne hyperspectral remote sensing data with both rich spectral and spatial features can
effectively improve the classification accuracy of vegetation species. However, the spectral …
effectively improve the classification accuracy of vegetation species. However, the spectral …
Deep learning architecture for air quality predictions
With the rapid development of urbanization and industrialization, many developing countries
are suffering from heavy air pollution. Governments and citizens have expressed increasing …
are suffering from heavy air pollution. Governments and citizens have expressed increasing …
[HTML][HTML] Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models
T Xayasouk, HM Lee, G Lee - Sustainability, 2020 - mdpi.com
Many countries worldwide have poor air quality due to the emission of particulate matter (ie,
PM10 and PM2. 5), which has led to concerns about human health impacts in urban areas …
PM10 and PM2. 5), which has led to concerns about human health impacts in urban areas …
A CFCC-LSTM model for sea surface temperature prediction
Sea surface temperature (SST) prediction is not only theoretically important but also has a
number of practical applications across a variety of ocean-related fields. Although a large …
number of practical applications across a variety of ocean-related fields. Although a large …