Rapid advancement in the development of hyperspectral image analysis techniques has led
to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral …

In recent years, the combination of deep learning and remote sensing has been a boiling
state. However, because of the difference between remote sensing images and natural …

The large size of hyperspectral imaging poses a significant threat to its potential use in real
life due to the abundant information stored in it. The use of deep learning for such data …

Abstract Machine-based classification usually involves some computer programs, known as
algorithms, developed using several mathematical formulations to accelerate the automated …

In recent years, advances in deep learning have greatly promoted the development of
hyperspectral image (HSI) compression algorithms. However, most existing compression …

This paper presents a new lossless image compression method based on the learning of
pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and …

Rapid and continuous advancements in remote sensing technology have resulted in finer
resolutions and higher acquisition rates of hyperspectral images (HSIs). These …

In recent years, action recognition has become a popular and challenging task in computer
vision. Nowadays, two-stream networks with appearance stream and motion stream can …

The hyperspectral image compression scheme is a trade-off between the limited hardware
resources of the on-board platform and the ever-growing resolution of the optical …

When we compress a large amount of data, we face the problem of the time it takes to
compress it. Moreover, we cannot predict how effective the compression performance will …