most citedA Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction

9 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV20243 cited

Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends

Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti +2

Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This paper provides a comprehensive rev…

cs.CV2024

Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data

Daniel Kovac, Jan Mucha, Jon Alvarez Justo +7

This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) an…

cs.CV20245 cited

Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images

Jon Alvarez Justo, Milica Orlandic

Hyperspectral Imaging (HSI) is used in a wide range of applications such as remote sensing, yet the transmission of the HS images by communication data links becomes challenging du…

cs.CV20249 cited

A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction

Jon Alvarez Justo, Daniela Lupu, Milica Orlandic +2

Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in th…

cs.CV2023

An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite

Jon A. Justo, Joseph Garrett, Dennis D. Langer +3

Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding…