S20 Computer vision vs human perception in remote sensing image analysis: Time to move on Arianna Traviglia, Karsten Lambers The (slow) emergence of semi-automated or supervised detection techniques to identify anthropogenic features over remote sensing imagery have received mixed reception in the past decade, with critics stressing the superiority of human vision and the irreplaceability of hu...

We incorporate auditory-based features into an unconventional pattern classification system, consisting of a network of spiking neurones with dynamical and multiplicative synapses. Although the network does not need any training and is autonomous, the analysis is dynamic and capable of extracting multiple features and maps. The neural network allows computing a binary mask that acts as a dynami...

The prediction of solar flares is practical and scientific interest; however, many machine learning methods used for this task do not provide the physical explanations behind a model's performance. We made use two recently developed explainable artificial intelligence techniques called gradient-weighted class activation mapping (Grad-CAM) expected gradients (EG) to reveal decision-making proces...

Remote sensing hyperspectral and more generally spectral instruments are common tools to decipher surface features in Earth Planetary science. While linear mixture is the most approximation for compounds detection (mineral, water, ice, etc...), transfer of light atmospheric medium highly non-linear. The exact simulation non-linearities can be estimated at very high numerical cost. Here I propos...

Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-tohigh frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of the epilarynx t...