Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About

About

Professor Auxiliar com Agregação da Universidade de Trás-os-Montes e Alto Douro (UTAD) e doutorado em Ciências da Engenharia Geográfica, pela Universidade do Porto e pela Universidade de Delft (Holanda), tendo apresenta a tese “Potential of integrating PSInSAR Methodologies in the Detection of Surface Deformation”. Atualmente, é Investigador (membro integrado) do Centre for Robotics in Industry and Intelligent Systems (CRISS), do INESC TEC/Polo UTAD, e investigador (colaborador) do CITAB (Centre for the Research and Technology of Agro-Environmental and Biological Sciences). Nos últimos anos tem-se dedicado, sobretudo, à utilização de Veículos Aéreos Não Tripulados (UAV) para aplicações agroflorestais. Utiliza imagens aéreas de elevada resolução, obtidas por diferentes sensores (RGB, NIR, Multiespectrais, Hiperespectrais e Térmicos) para, usando técnicas de processamento de imagem e desenvolvimento de algoritmos, extrair informações e parâmetros relevantes, sobretudo, na vinha, soutos e olivais. Estas técnicas são, no entanto, extensíveis à deteção e monitorização de grande parte das espécies arbóreas, que integram as nossas florestas, e de vegetação rasteira. É autor de várias publicações em revistas internacionais da especialidade do Remote Sensing. Participa em vários projetos de investigação, destacando-se o PARRA (Plataforma integrAda de monitoRização e avaliação da doença da flavescência douRada na vinha), em que é líder por parte da UTAD (SI I&DT, aviso Nº 08/SI/2015, Projeto em Co-Promoção, parceiros do projeto: TEKEVER ASDS - empresa líder, UTAD, Instituto Politécnico de Viana do Castelo, INIAV, Agrociência. Montante total atribuído 1.602.245,58€) e é membro do projeto Plataforma de Inovação da Vinha e do Vinho, linha Remote sensing and detection of grapevine diseases (Projeto I&DT pelo Norte2020, com um financiamento global de 4.500.000,00 €).

Interest
Topics
Details

Details

004
Publications

2023

Acacia dealbata classification from aerial imagery acquired using unmanned aerial vehicles

Authors
Pinto, J; Sousa, A; Sousa, J; Peres, E; Padua, L;

Publication
Procedia Computer Science

Abstract

2023

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

Authors
Teixeira, I; Morais, R; Sousa, JJ; Cunha, A;

Publication
AGRICULTURE-BASEL

Abstract
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

2023

Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data

Authors
Guimaraes, N; Padua, L; Sousa, JJ; Bento, A; Couto, P;

Publication
INTERNATIONAL JOURNAL OF REMOTE SENSING

Abstract
In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.

2023

MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)

Authors
Marchamalo-Sacristán, M; Ruiz-Armenteros, AM; Lamas-Fernández, F; González-Rodrigo, B; Martínez-Marín, R; Delgado-Blasco, JM; Bakon, M; Lazecky, M; Perissin, D; Papco, J; Sousa, JJ;

Publication
Remote Sensing

Abstract
The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of -6 mm/year (1992–2000), -4 mm/year (2002–2010), and -2 mm/year (2015–2021) with median values of -2.6 and -3.0 mm/year in the first periods (ERS and Envisat) and -1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring.

2022

Vineyard classification using OBIA on UAV-based RGB and multispectral data: A case study in different wine regions

Authors
Padua, L; Matese, A; Di Gennaro, SF; Morais, R; Peres, E; Sousa, JJ;

Publication
COMPUTERS AND ELECTRONICS IN AGRICULTURE

Abstract
Vineyard classification is an important process within viticulture-related decision-support systems. Indeed, it improves grapevine vegetation detection, enabling both the assessment of vineyard vegetative properties and the optimization of in-field management tasks. Aerial data acquired by sensors coupled to unmanned aerial vehicles (UAVs) may be used to achieve it. Flight campaigns were conducted to acquire both RGB and multispectral data from three vineyards located in Portugal and in Italy. Red, green, blue and near infrared orthorectified mosaics resulted from the photogrammetric processing of the acquired data. They were then used to calculate RGB and multispectral vegetation indices, as well as a crop surface model (CSM). Three different supervised machine learning (ML) approaches-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were trained to classify elements present within each vineyard into one of four classes: grapevine, shadow, soil and other vegetation. The trained models were then used to classify vineyards objects, generated from an object-based image analysis (OBIA) approach, into the four classes. Classification outcomes were compared with an automatic point-cloud classification approach and threshold-based approaches. Results shown that ANN provided a better overall classification performance, regardless of the type of features used. Features based on RGB data showed better performance than the ones based only on multispectral data. However, a higher performance was achieved when using features from both sensors. The methods presented in this study that resort to data acquired from different sensors are suitable to be used in the vineyard classification process. Furthermore, they also may be applied in other land use classification scenarios.

Supervised
thesis

2021

Classificação automática de castas de uva utilizando Deep Learning

Author
Gabriel António Pereira Santos Carneiro

Institution
UTAD

2021

Automatic detection of earthquake's deformations in SAR interferograms

Author
Bruno Miguel Ferreira Silva

Institution

2021

Automatic analysis of UAS-based multi-temporal data as support to a precision agroforestry management

Author
Luís Filipe Machado Pádua

Institution
UTAD

2021

Artificial intelligence techniques applied to agriculture

Author
Nuno Leandro Soares de Figueiredo

Institution
UTAD

2021

Hyperspectral data analysis for agriculture applications

Author
Jonás Hruska

Institution
UTAD