925 citations · 1.2k across the 10 of their papers we have counts for
14 papers
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…
Core-Set Selection for Data-efficient Land Cover Segmentation
Keiller Nogueira, Akram Zaytar, Wanli Ma +9
The increasing accessibility of remotely sensed data and their potential to support large-scale decision-making have driven the development of deep learning models for many Earth O…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
Diogo Nunes Goncalves, Jose Marcato Junior, Pedro Zamboni +4
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with indepe…
GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes
Penny Johnston, Keiller Nogueira, Kevin Swingler
Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights. These weights cannot be easily alter…
Facing the Void: Overcoming Missing Data in Multi-View Imagery
Gabriel Machado, Keiller Nogueira, Matheus Barros Pereira +1
In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from…
AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification
Gabriel Machado, Edemir Ferreira, Keiller Nogueira +3
It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always looking from above, some applications…
Fully Convolutional Open Set Segmentation
Hugo Oliveira, Caio Silva, Gabriel L. S. Machado +2
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches. However, these methods trained in a Cl…
Towards Open-Set Semantic Segmentation of Aerial Images
Caio C. V. da Silva, Keiller Nogueira, Hugo N. Oliveira +1
Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or ca…
An Introduction to Deep Morphological Networks
Keiller Nogueira, Jocelyn Chanussot, Mauro Dalla Mura +1
The recent impressive results of deep learning-based methods on computer vision applications brought fresh air to the research and industrial community. This success is mainly due…
Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
Keiller Nogueira, Jefersson A. dos Santos, Nathalia Menini +3
Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plan…
Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot +2
Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific…
Exploiting ConvNet Diversity for Flooding Identification
Keiller Nogueira, Samuel G. Fadel, Ícaro C. Dourado +7
Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure towards flood monitoring is bas…
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
Keiller Nogueira, Otávio A. B. Penatti, Jefersson A. dos Santos
We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were tra…