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Luca Cicala

Researcher at Italian Aerospace Research Centre

Publications -  32
Citations -  305

Luca Cicala is an academic researcher from Italian Aerospace Research Centre. The author has contributed to research in topics: Multispectral image & Motion estimation. The author has an hindex of 9, co-authored 27 publications receiving 251 citations.

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Journal ArticleDOI

Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy

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

Application of DInSAR Technique to High Coherence Sentinel-1 Images for Dam Monitoring and Result Validation Through In Situ Measurements

TL;DR: A highly reproducible DInSAR workflow that can be effectively used for dam monitoring is proposed, by validating its results with in situ measurements on some significant case studies in Italy.
Proceedings Article

UAV position and attitude estimation using IMU, GNSS and camera

TL;DR: A method for integration of measurements provided by inertial sensors, GPS and a video system in order to estimate position and attitude of an UAV (Unmanned Aerial Vehicle).
Proceedings ArticleDOI

SAR/multispectral image fusion for the detection of environmental hazards with a GIS

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.
Journal ArticleDOI

Low-complexity compression of multispectral images based on classified transform coding

TL;DR: Experiments carried out on several multispectral images show that the resulting unsupervised coder has a fully acceptable complexity, and a rate-distortion performance which is superior to that of the original supervised coder, and comparable to the best coders known in the literature.