Remote sensing data have become very widespread in recent years, and the exploitation of
this technology has gone from developments mainly conducted by government intelligence …

This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …

Advances in sensor and computer technology are revolutionizing the way remotely sensed
data is collected, managed and analyzed. In particular, many current and future applications …

Remotely sensed hyperspectral imaging instruments are capable of collecting hundreds of
images corresponding to different wavelength channels for the same area on the surface of …

Onboard real-time processing of remote sensing satellites is an important means of rapidly
obtaining information, and the fusion processing of panchromatic and multispectral data is of …

Hyperspectral imaging is a growing area in remote sensing in which an imaging
spectrometer collects hundreds of images (at different wavelength channels) for the same …

Cloud detection of satellite imagery is very important for quantitative remote sensing
research and remote sensing applications. However, many satellite sensors do not have …

Remote sensing imagery has an ever-increasing impact on important downstream
applications, such as vegetation monitoring and climate change modelling. Clouds …

With the development of remote-sensing technology, optical remote-sensing imagery
processing has played an important role in many application fields, such as geological …

Cloud detection is an important task in remote sensing (RS) image processing. Numerous
cloud detection algorithms have been developed. However, most existing methods suffer …