3.2k citations · 11.3k across the 87 of their papers we have counts for
6 papers · 1 filter
Rotation equivariant vector field networks
Diego Marcos, Michele Volpi, Nikos Komodakis +1
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of tr…
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Michele Volpi, Devis Tuia
Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial da…
Non-convex regularization in remote sensing
Devis Tuia, Remi Flamary, Michel Barlaud
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or s…
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions
Devis Tuia, Rémi Flamary, Nicolas Courty
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and…
Learning rotation invariant convolutional filters for texture classification
Diego Marcos, Michele Volpi, Devis Tuia
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights…
Principal Polynomial Analysis
Valero Laparra, Sandra Jiménez, Devis Tuia +2
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principa…