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

Hyperspectral imaging is an indispensable technology for many remote sensing
applications, yet expensive in terms of computing resources. It requires significant …

With the continuous development of science and technology, the information age has
arrived, and people have fully entered the information age. In this new era, Internet …

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 …

Memory management and coding complexity are the major challenging issues of any
hyperspectral image sensor. The hyperspectral image compression algorithm plays a …

The 3D-zero memory set partitioned embedded block (3D-ZM-SPECK) is an embedded and
memory-efficient compression algorithm. Through, the 3D-ZM-SPECK does not require any …

Memory management of the hyperspectral image sensor is a challenging issue. The existing
hyperspectral image compression schemes play a dominant role in minimizing the cost of …

This study addresses image denoising alongside the compression and reconstruction of
hyperspectral images (HSIs) using deep learning techniques, since the research community …

Complexity of any onboard hyperspectral image sensor is a challenging issue. The existing
hyperspectral image compression algorithm plays a great role in reducing the data …

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