9 citations · 17 across the 5 of their papers we have counts for
7 papers
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…
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Diana-Nicoleta Grigore, Mariana-Iuliana Georgescu, Jon Alvarez Justo +3
Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In…
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…
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…
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…
Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Jon Alvarez Justo, Alexandru Ghita, Daniel Kovac +5
Satellites are increasingly adopting on-board AI to optimize operations and increase autonomy through in-orbit inference. The use of Deep Learning (DL) models for segmentation in h…