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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.

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Details

Details

002
Publications

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.

2017

UAV cooperative perception for target detection and tracking in maritime environment

Authors
Amaral, G; Silva, H; Lopes, F; Ribeiro, JP; Freitas, S; Almeida, C; Martins, A; Almeida, J; Silva, E;

Publication
OCEANS 2017 - Aberdeen

Abstract