Recent advances in remote sensing technologies have led to the fast proliferation of
massive and often imbalanced datasets. Direct classification in these datasets becomes …

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote
sensing data using multiple statistical and machine-learning methods. The effect of filter …

Hyperspectral imaging is an area of active research with many applications in remote
sensing, mineral exploration, and environmental monitoring. Deep learning and, in …

The classification process is one of the most crucial processes in hyperspectral imaging.
One of the limitations in classification process using machine learning technique is its …

In recent years, using oceans of data and virtually infinite cloud-based computation power,
deep learning models leverage the current state-of-the-art classification to reach expert level …

This paper proposes an improved KNN algorithm to overcome the class overlap**
problem when the class distribution is skewed. Different from the conventional KNN …