In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo‐SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in‐situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L‐, C‐ and X‐bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non‐forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (°) was computed for each sensor‐polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L‐ and X‐ bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.
Lapini, A., Pettinato, S., Santi, E., Paloscia, S., Fontanelli, G., Garzelli, A. (2020). Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas. REMOTE SENSING, 12(3) [10.3390/rs12030369].
Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas
Garzelli, Andrea
2020-01-01
Abstract
In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo‐SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in‐situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L‐, C‐ and X‐bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non‐forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (°) was computed for each sensor‐polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L‐ and X‐ bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.File | Dimensione | Formato | |
---|---|---|---|
remotesensing-12-00369.pdf
accesso aperto
Descrizione: RS-2020-forest
Tipologia:
PDF editoriale
Licenza:
Creative commons
Dimensione
6.24 MB
Formato
Adobe PDF
|
6.24 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/1091081