SAR sensors measurements for environmental classification: Machine learning-based performancesShow others and affiliations
2020 (English)In: IEEE Instrumentation & Measurement Magazine, ISSN 1094-6969, E-ISSN 1941-0123, Vol. 23, no 6, p. 23-30, article id 9200877Article in journal (Refereed) Published
Abstract [en]
Artificial intelligence, in particular a supervised and unsupervised machine learning approach, has been becoming an interest in the field of measurement and instrumentation. Many problems of classification can be faced by a machine learning approach. We know machine learning is a broad area of artificial intelligence that comprises some other lines of research and activities such as deep learning. Synthetic aperture radar (SAR) measurements by means of its sensors are of great interest in environmental monitoring, in particular in land classification. This paper presents findings related to measurements and characterization through land classification of an environmentally sensitive area in Italy over two different time periods in order to assess changing parameters. A deep learning algorithm has been designed and implemented, and a comparison has been established with a spectral density approach.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020. Vol. 23, no 6, p. 23-30, article id 9200877
Keywords [en]
Deep learning, Learning systems, Spectral density, Synthetic aperture radar, Turing machines, Changing parameter, Environmental classifications, Environmental Monitoring, Environmentally sensitive areas, Field of measurements, Machine learning approaches, Time-periods, Unsupervised machine learning, Learning algorithms
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-41523DOI: 10.1109/mim.2020.9200877ISI: 000576252000005Scopus ID: 2-s2.0-85095969709OAI: oai:DiVA.org:miun-41523DiVA, id: diva2:1536264
2021-03-102021-03-102021-04-27Bibliographically approved