Hyperspectral images have its applications in various fields. Here, hyperspectral image from PRISMA which is a fundamental satellite of Italian Space Agency is being used for discriminating the wildfire fuel types on Sardinian Island of Italy. PRISMA is an on-demand mission and the available data in the archive are limited. There is no literature available on land use/vegetation classification using PRISMA data. In this paper, a new approach for generating samples to form a dataset for classifying the wildfire fuels and for classifying mixed pixels using iso-bioclimatic conditions are proposed. The classified map created using the dataset and using the iso-bioclimatic conditions is been validated. From the accuracy assessment, SVM classifier showed an overall accuracy of 86% and kappa coefficient of 0.79. Then, the classified map is converted into fuel map. This study suggests that the proposed approach can be used to generate samples for land use/vegetation classification and to assign vegetation types to mixed pixels depending upon the iso-bioclimatic conditions.
New Approach of Sample Generation and Classification for Wildfire Fuel Mapping on Hyperspectral (Prisma) Image / Shaik, RIYAAZ UDDIEN; Laneve, Giovanni; Fusilli, Lorenzo. - 21227329(2021), pp. 5417-5420. (Intervento presentato al convegno IGARSS2021 tenutosi a Brussels, Belgium) [10.1109/IGARSS47720.2021.9554652].
New Approach of Sample Generation and Classification for Wildfire Fuel Mapping on Hyperspectral (Prisma) Image
Riyaaz Uddien Shaik
Primo
Methodology
;Giovanni Laneve
Secondo
Supervision
;Lorenzo Fusilli
Ultimo
Data Curation
2021
Abstract
Hyperspectral images have its applications in various fields. Here, hyperspectral image from PRISMA which is a fundamental satellite of Italian Space Agency is being used for discriminating the wildfire fuel types on Sardinian Island of Italy. PRISMA is an on-demand mission and the available data in the archive are limited. There is no literature available on land use/vegetation classification using PRISMA data. In this paper, a new approach for generating samples to form a dataset for classifying the wildfire fuels and for classifying mixed pixels using iso-bioclimatic conditions are proposed. The classified map created using the dataset and using the iso-bioclimatic conditions is been validated. From the accuracy assessment, SVM classifier showed an overall accuracy of 86% and kappa coefficient of 0.79. Then, the classified map is converted into fuel map. This study suggests that the proposed approach can be used to generate samples for land use/vegetation classification and to assign vegetation types to mixed pixels depending upon the iso-bioclimatic conditions.File | Dimensione | Formato | |
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