Mapping Native Vegetation In Mato Grosso Do Sul

This project funded by CNPq proposes developing methods based on machine learning for mapping tree species protected by law in Mato Grosso do Sul (MS) from images collected by UAVs (Unmanned Aerial Vehicles) and for mapping native vegetation throughout the state from orbital images. Continue reading →

Deep Learning Techniques for Remote Sensing Image Analysis

The project is funded by FAPERJ and investigates methods based on deep learning (deep learning-DL) for semantic segmentation of remote sensing (SR) images. Continue reading →

Signal Processing and Machine Learning with Sensing Applications and Wireless Communications Systems

The project is funded by FAPERJ and investigates signal processing and machine learning techniques and their applications in remote sensing and sensor systems and wireless communications. Continue reading →

Improving Deforestation Detection Using Domain Adaptation Techniques

The project aims to evaluate and develop domain adaptation methods to the generalization improvement of deforestation detection techniques in remote sensing images. Read more at project site.

Deep Learning for Semantic Segmentation of Remote Sensing Data

The present project aims to investigate different strategies to handle the insufficient availability of labeled training data, towards fully exploiting the potential of deep architectures for remote sensing image analysis.
Regarding the application domain, the project aims to employ the solutions to be developed in relevant, concrete problems:
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Long-term Face Tracking

The present project focus on the development of a framework aiming the long-term face tracking under unconstrained scenarios in video sequences. The main idea is to step up the tracking processing by combining a tracking ensemble with the information delivered by a face detector, and also by including a feedback process to provide additional input to the trackers.

Crop Mapping from Multitemporal SAR Image Sequences Using Deep Learning Techniques

The project aims to evaluatie and compare state-of-the-ate Deep Learning techinques for crop mapping in tropical and a temperate regions. It is done using multi-temporal SAR image sequences.

Synthesis of Multispectral Optical Images from SAR/OPTICAL Multi-Temporal data using cGANS

The project aims to research about synthesizing of multispectral optical imagery using Generative Adversarial networks. You can find more information at the Project Site

Crop Recognition based on Conditional Random Fields

This project aims to recognize crop types for crop yield estimation using Conditional Random Fields. You can find more information at the Project Site

Deforestation detection in the Brazilian Amazon

This project aims to evaluate deep learning techniques applied to Deforestation Detection in the Brazilian Amazon. You can find more information at the Project Site

Deep Learning for Spatio-Temporal Semantic Segmentation in Agricultural Crop Recognition Applications

This project aims to evaluate and propose deep learning techniques applied to the semantic segmentation of sequences of satellite images applied to agricultural crop recognition in tropical regions from Brazil. Specifically, the project focuses on unique, end-to-end architectures capable of producing the segmentation outcome of entire image sequences. Read more at the Project Site

Deep Unsupervised Hyperspectral Classification

This research aims to map Savannahs physiognomies over hyperspectral images, using unsupervised deep learning techniques.

Face Anti-spoofing

This research aims to evaluate and develop fraud detection methods (anti-spoofing) in biometric systems.

The scope mainly involves the face recognition cases.

LIVENESS – Métodos de Proteção contra Fraudes em Sistemas Biométricos Multimodais

The project aims to evaluate and develop fraud detection methods (spoofing) in multimodal biometric systems. The scope mainly involves the combination of facial and voice recognition.

InterIMAGE Cloud Platform

The main objective of the Interimage Cloud Platform is the scientific and technological development in the remote sensing (SR) image analysis, focused on the object based image analysis (GEOBIA) and cloud computing . In terms of scientific development, the main topics of the Project are: (1) the representation and processing of knowledge explicitly represented on an interpretation of SR images; (2) methods of segmentation of SR images, feature extraction and object classification in distributed environments; (3) methods of multitemporal analysis of SR images in distributed environments; (4) methods of advanced visualization of images and objects stored in a distributed way in computer clusters. In terms of technological development, the main objective is the creation of the Interimage Cloud Platform, an innovative platform for GEOBIA, structured on an architecture based on the cloud computing paradigm, in which will be applied the results of the research developed during this project.

Multimodal Biometric Recognition

The objectives of this research work are:

Evaluate state-of-the-art technologies for speaker recognition.
Investigate multimodal fusion techniques.
Develop and test a prototype of a speaker recognition system.
Integrate speaker recognition into the face recognition prototype existing in Computer Vision Laboratory – LVC.

Read more at Multimodal Biometric Recognition

INTERSAR Project

The main objective of INTERSAR is to develop in the scope of InterIMAGE, a free open source software platform for GEOBIA applications, methodologies, algorithms and computational tools for automatic Land Use and Land Cover classification upon optical and SAR (synthetic aperture radar) data.

RADAR MONITOR – Monitoring from RADAR Data

The general objective of the RADAR MONITOR project is to provide solutions for the systematic monitoring of changes on the Earth’s surface from remote sensing images, with emphasis on data provide by space born SAR (Synthetic-aperture radar) sensors. More precisely, the Project aims to extend the InterIMAGE platform, by incorporating specific software operators for the multitemporal analysis of SAR as well as optical data.

Brazilian Visualization Network – Geoinformation

The Brazilian Visualization Network (RVA), whose general coordination is under the Brazilian National Laboratory of Scientific Computation (LNCC), is part of the Brazilian Technology System (SIBRATEC) of the Ministry of Science, Technology and Information (MCTI). The RVA aims at the integration of the academy to foster the innovation and creation of new markets for technological products and services. Beyond LNCC, the RVA involves the following institutions: PUC-Rio, PUC-RS, UFRS, UFPE, UFPB, INPE, USP, UFRJ e UNICAMP, whereby LNCC, UFRJ, PUC-RS, UFPE, USP and PUC-Rio constitute the so called Coordination Nucleus of RVA.

TOLOMEO

The project “Tools for Open Multi-Risk Assessment using Earth Observation Data” (TOLOMEO) is funded under the Marie Curie International Research Staff Exchange Scheme (PIRSES-GA-2009) with the ultimate goal to establish an international cooperation between partners in Europe and South-America focused on the development of free tools for remotely sensed data analysis.