Common methods of ocean remote sensing and seafloor surveying are mainly carried out
by airborne and spaceborne hyperspectral imagers. However, the water column hinders the …

Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep
learning methods driven by massive data cannot achieve the intended expectations. Semi …

Earth is experiencing unprecedented climate change driven by anthropogenic activities. The
Paris Climate Agreement is the most recent international agreement pushing nations to …

Hyperspectral image (HSI) classification has recently been successfully explored by using
deep learning (DL) methods. However, DL models rely heavily on a large number of labeled …

Hyperspectral imagers enable the collection of high-resolution spectral images exploitable
for the supervised classification of habitats and objects of interest (OOI). Although this is a …

This paper is a section of several preliminary studies of the Underwater Drones Group of the
Università degli Studi “Roma Tre” Science Department: We describe the study philosophy …

Deep learning DL techniques have recently been used to examine the classification of
remote sensing data like hyperspectral images HSI. However, DL models are difficult to …

Colonies of the endangered red sea pen Pennatula rubra (Cnidaria: Pennatulacea)
sampled by trawling in the northwestern Mediterranean Sea were analyzed. Biometric …

Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of
underwater life. However, a severe decline in the coral resources can be noted in the last …

In the field of hyperspectral image (HSI) classification, a source dataset with ample labeled
samples is commonly utilized to enhance the classification performance of a target dataset …