Exame de Qualificação: New Deep Learning Strategies for Deforestation Monitoring using Change Detection and Data Fusion Architectures

Orientadores:
Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Pablo Javier Blanco - Laboratório Nacional de Computação Científica - LNCC (presidente)
Bruno Richard Schulze - Laboratório Nacional de Computação Científica - LNCC
Raul Queiroz Feitosa - Pontifícia Universidade Católica do Rio de Janeiro - PUC-RIO

Suplentes:
Roberto Pinto Souto - Laboratório Nacional de Computação Científica - LNCC

Resumo:

CHANGE DETECTION CAPTURES SPATIAL DIFFERENCES IN THE STATE OF AN OBJECT BY OBSERVING IT AT DIFFERENT TIMES. IN REMOTE SENSING, ITS FUNCTION IS TO MONITOR ENVIRONMENTAL CHANGES BY JOINTLY PROCESSING IMAGES OF THE SAME GEOGRAPHICAL ARE A ACQUIRED AT DIFFERENT DATES , WHICH IS ESSENTIAL FOR THE MANAGEMENT OF NATURAL RESOURCES, THE CONSERVATION OF ECOSYSTEMS AND BIODIVERSITY AS WELL AS DECISION SUPPORT FOR SUSTAINABLE DEVELOPMENT. CHANGE DETECTION USING MULTITEMPORAL REMOTE SENSING IMAGERY PLAYS A CRUCIAL ROLE IN NUMEROUS FIELDS OF APPLICATIONS, WHICH INCLUDES DEFORESTATION. DEEP LEARNING (DL) HAS BECOME PREVALENT IN THE LAST FEW YEARS, INCLUDING REMOTE SENSING, FOR THE ABILITY TO LEARN DISCRIMINATIVE REPRESENTATIONS DIRECTLY FROM RAW DATA. REGARDING DEFORESTATION DETECTION, NOVEL DL-BASED APPROACHES HAVE BEEN RECENTLY PROPOSED FOR THIS TASK. THE PROPOSAL FOR THIS WORK IS THE DEVELOPMENT OF NEURAL NETWORK ARCHITECTURES FOR DEFORESTATION DETECTION THAT INCORPORATE A CONVOLUTIONAL LSTM (LONG SHORT-TERM MEMORY) IN A RESIDUAL LEARNING UNIT, TO BE USED IN AN ENCODER–DECODER BACKBONE. RECURRENT NEURAL NETWORKS, SUCH AS LSTM, ARE USEFUL FOR PROCESSING MULTITEMPORAL DATA DUE TO THEIR ABILITY TO DEAL WITH SEQUENTIAL INFORMAT ION AND CAPTURE LONG-TERM DEPENDENCIES, ALLOWING THE EXTRACTION OF MEANINGFUL INFORMATION AND THE PERFORMANCE OF COMPLEX TASKS THAT DEPEND ON THE ANALYSIS OF DATA SEQUENCES. RESIDUAL LEARNING, APPLIED IN ARCHITECTURES SUCH AS RESUNET, ALLOWS THE NETWORK TO FOCUS ON SPECIFIC CHANGES BETWEEN IMAGES. IN THE CONTEXT OF DEFORESTATION DETECTION, IT IS ESSENTIAL TO IDENTIFY THE CHANGES THAT OCCUR IN THE MONITORED AREAS OVER TIME. IN THIS SENSE, THE LSTM RECURRENT NETWORK IS USED TO EXTRACT TEMPORAL FEATURES AND THE RESIDUAL LEARNING TO ACQUIRE SPATIAL INFORMATION. THE PROPOSED ARCHITECTURES WILL BE TESTED USING SAR (SYNTETHIC APERTURE RADAR) AND OPTICAL IMAGES AVAILABLE IN PUBLIC DATASETS, WITH COMPARISON WITH STATE-OF-THE-ART APPROACHES. BESIDES, TECHNIQUES FOR IMAGE FUSION WILL BE TESTED TO GENERATE THE INPUT FOR THE DEVELOPED NETWORKS WITH DATA OBTAINED WITH VARIED SENSORS. ALSO, IN THE CASE OF SAR IMAGES, NEW STRATEGIES FOR DATA AUGMENTATION WILL BE EXPERIMENTED.