Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Hyperspectral remote sensing technology has advanced significantly in the past two
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …

Sporadic ocean color events with characteristic spectra, in particular algal blooms, call for
quick delivery of high-resolution remote sensing data for further analysis. Motivated by this …

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

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of
remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) …

The visual inspection of histopathological samples is the benchmark for detecting breast
cancer, but a strenuous and complicated process takes a long time of the pathologist …

A decision tree is a well-known machine learning technique. Recently their popularity has
increased due to the powerful Gradient Boosting ensemble method that allows to gradually …

Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which
can recover a large range of scenes with a small number of sensors via compressed …

Hyperspectral imaging (HSI) can extract information from scenes on the earth surface
acquired by airborne or spaceborne sensors. On-board processing of HSI is characterized …

Remotely sensed hyperspectral imaging is a very active research area, with numerous
contributions in the recent scientific literature. The analysis of these images represents an …