arXiv subjects

Flavio Piccoli

Publications and source records attributed to Flavio Piccoli.

5 recordsLinked to original sources

arXiv2023

Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset

In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the art. This dataset, collected using the ASI PRISMA satellite, covers about 262200 km2, and its heterogeneity is granted by randomly sampling the Earth's soil. Second, we adapted several state of the art approaches based on deep learning to fit PRISMA hyperspectral data and then assessed, quantitatively and qualitatively, the performance in this new scenario. The investigation has included two settings: Reduced Resolution (RR) to evaluate the techniques in a supervised environment and Full Resolution (FR) for a real-world evaluation. The main purpose is the evaluation of the reconstruction fidelity of the considered methods. In both scenarios, for the sake of completeness, we also included machine-learning-free approaches. From this extensive analysis has emerged that data-driven neural network methods outperform machine-learning-free approaches and adapt better to the task of hyperspectral pansharpening, both in RR and FR protocols.

eess.IV↗

arXiv2023

Semi-supervised cross-lingual speech emotion recognition

Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques. However, cross-lingual SER remains a challenge in real-world applications due to two main factors: the first is the big gap among the source and the target domain distributions; the second factor is the major availability of unlabeled utterances in contrast to the labeled ones for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when only few labeled examples in the target domain (i.e. the new language) are available. Our method is based on a Transformer and it adapts to the new domain by exploiting a pseudo-labeling strategy on the unlabeled utterances. In particular, the use of a hard and soft pseudo-labels approach is investigated. We thoroughly evaluate the performance of the proposed method in a speaker-independent setup on both the source and the new language and show its robustness across five languages belonging to different linguistic strains. The experimental findings indicate that the unweighted accuracy is increased by an average of 40% compared to state-of-the-art methods.

cs.SD↗

arXiv2022

A unified platform for GIS data management and analytics

In this work we propose a software platform for the collection, visualization, management and analysis of heterogeneous and multisource data for soil characteristics estimation. The platform is designed in such a way that it can easily handle proximity, airborne and spaceborne data and provides to the final user all the tools and proper visualizations to perform precision agriculture. The proposed software allows an easy integration of new data (that can be performed directly on-board of the acquisition device) and the possibility to add custom predictive systems for soil characteristic estimation. Usability experiments show that the platform is easy to use and effective.

cs.CY↗

arXiv2022

A deep scalable neural architecture for soil properties estimation from spectral information

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it permits to backtrace the spectral bands that most contribute to the estimation of a given variable; (iii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. 'Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution.

cs.CV↗

arXiv2022

Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels

In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried out. Performance are measured in terms of normalized mutual information, adjusted Rand index and F1-score. Results demonstrate the validity of the proposed method in comparison with the state of the art.

cs.CV↗