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Giuseppe Masi
Researcher at University of Naples Federico II
Publications - 14
Citations - 968
Giuseppe Masi is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Image segmentation & Segmentation-based object categorization. The author has an hindex of 7, co-authored 14 publications receiving 588 citations. Previous affiliations of Giuseppe Masi include Information Technology University.
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Journal ArticleDOI
Pansharpening by Convolutional Neural Networks
TL;DR: A new pansharpening method is proposed, based on convolutional neural networks, which is largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.
Journal ArticleDOI
Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images
TL;DR: Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.
Journal ArticleDOI
Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy
Angela Errico,Cesario Vincenzo Angelino,Luca Cicala,G. Persechino,C. Ferrara,Massimiliano Lega,Andrea Vallario,Claudio Parente,Giuseppe Masi,Raffaele Gaetano,Giuseppe Scarpa,Donato Amitrano,Giuseppe Ruello,Luisa Verdoliva,Giovanni Poggi +14 more
TL;DR: A fast and easy-to-use system has been realized based on a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment.
Journal ArticleDOI
Exploration of Multitemporal COSMO-SkyMed Data via Interactive Tree-Structured MRF Segmentation
Raffaele Gaetano,Donato Amitrano,Giuseppe Masi,Giovanni Poggi,Giuseppe Ruello,Luisa Verdoliva,Giuseppe Scarpa +6 more
TL;DR: This work proposes a new approach for remote sensing data exploration, based on a tight human-machine interaction, and tests the proposed approach for the exploration of multitemporal COSMO-SkyMed data, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods.
Proceedings ArticleDOI
SAR/multispectral image fusion for the detection of environmental hazards with a GIS
Angela Errico,Cesario Vincenzo Angelino,Luca Cicala,Dominik Patryk Podobinski,G. Persechino,C. Ferrara,Massimiliano Lega,Andrea Vallario,Claudio Parente,Giuseppe Masi,Raffaele Gaetano,Giuseppe Scarpa,Donato Amitrano,Giuseppe Ruello,Luisa Verdoliva,Giovanni Poggi +15 more
TL;DR: In this article, a GIS-based methodology, using optical and SAR remote sensing data, together with more conventional sources, was proposed for the detection of small cattle breeding areas, potentially responsible of hazardous littering.