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About

About

In 2011 I joined Instituto Superior de Engenharia do Porto in the Bachelor’s degree in Electrical and Computer Engineering, and graduated in 2014.

Then, I started the master's degree in Electrical and Computer Engineering, branch of Autonomous Systems, having obtained the master's degree in 2016.

At the same time, I started my work at INESC TEC, where I have been part of the SUNNY project team, developing data processing techniques for hyperspectral cameras.

Interest
Topics
Details

Details

002
Publications

2021

Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection

Authors
Freitas, S; Silva, H; Silva, E;

Publication
Remote Sensing

Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

2019

Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

Authors
Freitas, S; Silva, H; Almeida, JM; Silva, E;

Publication
International Journal of Advanced Robotic Systems

Abstract
This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications. © The Author(s) 2019.

2018

Supervised classification for hyperspectral imaging in UAV maritime target detection

Authors
Freitas, S; Almeida, C; Silva, H; Almeida, J; Silva, E;

Publication
18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018

Abstract
This paper addresses the use of a hyperspectral image system to detect vessels in maritime operational scenarios. The developed hyperspectral imaging classification methods are based on supervised approaches and allow to detect the presence of vessels using real hyperspectral data. We implemented two different methods for comparison purposes: SVM and SAM. The SVM method, which can be considered one of most utilized methods for image classification, was implemented using linear, RBF, sigmoid and polynomial kernels with PCA for dimensionality reduction, and compared with SAM using a two classes definition, namely vessel and water. The obtained results using real data collected from a UAV allow to conclude that the SVM approach is suitable for detecting the vessel presence in the water with a precision and recall rates favorable when compared to SAM. © 2018 IEEE.

2018

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Authors
Freitas, S; Silva, H; Almeida, J; Silva, E;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-of- the-art method. © 2017 The Author(s)

2018

Supervised vs unsupervised approaches for real time hyperspectral imaging maritime target detection

Authors
Freitas, S; Silva, H; Almeida, J; Martins, A; Silva, E;

Publication
2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018

Abstract
This paper addresses the use of supervised and unsupervised methods for classification of hyperspectral imaging data in maritime border surveillance domain. In this work supervised (SVM) and unsupervised (HYDADE) approaches were implemented. An evaluation benchmark was performed in order to compare methods results using real hyperspectral imaging data taken from an Unmanned Aerial Vehicle in maritime border surveillance scenario. © 2018 IEEE.