Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

In recent years, several powerful machine learning (ML) algorithms have been developed
for image classification, especially those based on ensemble learning (EL). In particular …

Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have
become popular in remote sensing (RS) image change detection (CD). However, CNNs …

Increasing numbers of explanatory variables tend to result in information redundancy and
“dimensional disaster” in the quantitative remote sensing of forest aboveground biomass …

Due to global efforts to reduce the rise in the average global temperature by replacing fossil
fuels, the amount of wind power installed worldwide is continuously increasing. The costs …

This paper focuses on the comparison of dimensionality reduction effect between LightGBM
and XGBoost-FA. With respect to XGBoost, LightGBM can be built in the effect of …

Demand forecasting has been a major concern of operational strategy to manage the
inventory and optimize the customer satisfaction level. The researchers have proposed …

Tree-based methods and deep neural networks (DNNs) have drawn much attention in the
classification of images. Interpretable canonical deep tabular data learning architecture …

Flash floods pose a major challenge in various regions of the world, causing serious
damage to life and property. Here we investigated the Izvorul Dorului river basin from …

Urban land use/land cover (LULC) classification has long been a hotspot for remote sensing
applications. With high spatio-temporal resolution and multispectral, the recently launched …