Robust hyperspectral image classification with rejection fields

In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyperspectral image classification is generally an ill-posed image problem where pixels may belong to unknown classes, and obtaining representative and complete training sets is costly. Furthermore, the need for high classification accuracies is frequently greater than the need to classify the entire image. We approach this problem with a robust classification method that combines classification with context with classification with rejection. A rejection field that will guide the rejection is derived from the classification with contextual information obtained by using the SegSALSA algorithm. We validate our method in real hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets.Comment: This paper was submitted to IEEE WHISPERS 2015: 7th Workshop on Hyperspectral Image and Signal Processing: Evolution on Remote Sensing. 5 pages, 1 figure, 2 table

Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning

Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data. However, FL faces a significant challenge in the form of heterogeneous data distributions among clients, which leads to a reduction in performance and robustness. A recent approach to mitigating the impact of heterogeneous data distributions is through the use of foundation models, which offer better performance at the cost of larger computational overheads and slower inference speeds. We introduce foundation model distillation to assist in the federated training of lightweight client models and increase their performance under heterogeneous data settings while keeping inference costs low. Our results show improvement in the global model performance on a balanced testing set, which contains rarely observed samples, even under extreme non-IID client data distributions. We conduct a thorough evaluation of our framework with different foundation model backbones on CIFAR10, with varying degrees of heterogeneous data distributions ranging from class-specific data partitions across clients to dirichlet data sampling, parameterized by values between 0.01 and 1.0.Comment: 6 Pages + Appendice

Robust Image Classification with Context and Rejection

Classifications systems are ubiquitous; despite efforts going into training andfeature selection, misclassifications occur and their effects can be critical. This isparticularly true in classification problems where overlapping classes, small or incompletetraining sets, and unknown classes occur. In this thesis, we mitigate misclassificationsand their effects by adapting the behavior of the classifier on sampleswith high potential for misclassification through the use of robust classificationschemes that combine context and rejection. We thus combine the advantages ofusing contextual priors in classification with those of classification with rejection. Inclassification with rejection, we are able to improve classification performance at theexpense of not classifying the entire data set.We thus add the following tools to the robust classification toolbox: 1) we deriveperformance measures for evaluating of classifiers with rejection; 2) we createa family of convex algorithms, SegSALSA, to classify with context; 3) we designarchitectures for robust classification with context and rejection that encompass interactionsbetween context and rejection. We validate our approach on two differentreal-world data sets: histopathological and hyperspectral images

Supervised Hyperspectral Image Segmentation: A Convex Formulation Using Hidden Fields

<p>Image segmentation is fundamentally a discrete problem. It consists of finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. The optimization is obtained via integer optimization which is NP-hard, apart from few exceptions. We sidestep from the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a hidden set of real-valued random fields determining the probability of a given partition. Armed with this model, the original discrete optimization is converted into a convex program. To infer the hidden fields, we introduce the Segmentation via the Constrained Split Augmented Lagrangian Shrinkage Algorithm (SegSALSA). The effectiveness of the proposed methodology is illustrated with hyperspectral image segmentation.</p

Classification with reject option using contextual information

<p>We propose a new algorithm for classification that merges classification with reject option with classification using contextual information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance by including local and nonlocal contextual information, at the expense of rejecting a fraction of the samples. As a probabilistic model, we adopt a multinomial logistic regression. We use a discriminative random model for the description of the problem; we introduce reject option into the classification problem through association potential, and contextual information through interaction potential. We validate the method on the images of H&E-stained teratoma tissues and show the increase in the classifier performance when rejecting part of the assigned class labels.</p

Estudo do Impacto Socioeconómico da desativação da Central Termoelétrica de Sines

Este estudo, desenvolvido por uma equipa de investigação da Universidade de Évora e do Instituto Superior Técnico, decorre da decisão da EDP de encerrar a Central Termoelétrica de Sines em janeiro de 2021. Esta decisão está em linha com os objetivos nacionais e internacionais de se atingir a neutralidade carbónica em 2050. O estudo desenvolvido por iniciativa da EDP, tem por objetivo a análise da evolução socioeconómica de Sines e a identificação dos principais cenários alternativos de desenvolvimento da Região a curto e a médio prazo