The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the
advantages of each data, hence benefiting accurate land cover classification. However …

Most object detection methods based on remote sensing images are generally dependent
on a large amount of high-quality labeled training data. However, due to the slow acquisition …

Tactical reconnaissance using small unmanned aerial vehicles has become a common
military scenario. However, since their sensor systems are usually limited to rudimentary …

Conventional classification algorithms have shown great success in balanced hyperspectral
data classification. However, the imbalanced class distribution is a fundamental problem of …

Real-world datasets are often contaminated with label noise; labeling is not a clear-cut
process and reliable methods tend to be expensive or time-consuming. Depending on the …

Soybeans are one of the main crops grown in the United States. It is crucial to grasp the
distribution of soybean cultivation areas for ensuring food security, eradicating hunger and …

Forest fires are severe disasters that cause significant damage in the Republic of Korea and
the entire world, and an effort is being made to prevent forest fires internationally. The …

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR)
data has a wide range of applications. This paper proposes a novel feature fusion method …

Remote sensing image change detection is the key technology for monitoring forest windfall
damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques …

This work presents a novel convolutional neural network, termed multispectral features
network (MF-Net), for learning local feature descriptors in multispectral images. Unlike most …