Hora: 14h30

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

Banca Examinadora:
Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC (presidente)
Roberto Pinto Souto - Laboratório Nacional de Computação Científica - LNCC
José Marcato Junior - Universidade Federal de Mato Grosso do Sul - UFMS

Suplentes:
Bruno Richard Schulze - Laboratório Nacional de Computação Científica - LNCC
Raul Queiroz Feitosa - Pontifícia Universidade Católica do Rio de Janeiro - PUC-RIO

Resumo:

Obtaining comprehensive labeled data for remote sensing applications can be costly and time-consuming. So using traditional s upervised learning can become infeasible [1]. This dissertation explores weakly supervised learning (WSL) as a compelling alternative, focusing on multi-class semantic segmentation with limited label availability [2]. More specifically, we delve into the realm of semantic segmentation for remote sensing imagery while using the ISPRS Potsdam dataset [3] as a benchmark for comparing our WSL approach with the fully supervised method. Furthermore, we address the significance of class imbalance challenges [4] and the necessity of inferring reliable and representative high confidence zones for the proper functioning of the proposed system, which are crucial for robust performance. To further improvement, we investigate a new loss term designed specifically for weakly supervised learning. This novel function effectively takes advantage of information
previously discarded from unlabeled pixels.