This study shows a comparison between pixel-based and object-based approaches in data fusion of high-resolution multispectral GeoEye-1 imagery and high-resolution COSMO-SkyMed SAR data for land-cover/land-use classification. The per-pixel method consisted of a maximum likelihood classification of fused data based on discrete wavelet transform and a classification from optical images alone. Optical and SAR data were then integrated into an object-oriented environment with the addition of texture measurements from SAR and classified with a nearest neighbor approach. Results were compared with the classification of the GeoEye-1 data alone and the outcomes pointed out that per-pixel data fusion did not improve the classification accuracy, while the object-based data integration increased the overall accuracy from 73% to 89%. According to results, an object-based approach with the introduction of adjunctive information layers proved to be more performing in land-cover/land-use classification than standard pixel-based methods.

High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification

RUSMINI, MARCO;CANDIANI, GABRIELE;FRASSY, FEDERICO;MAIANTI, PIERALBERTO;MARCHESI, ANDREA;ROTA NODARI, FRANCESCO;GIANINETTO, MARCO
2012-01-01

Abstract

This study shows a comparison between pixel-based and object-based approaches in data fusion of high-resolution multispectral GeoEye-1 imagery and high-resolution COSMO-SkyMed SAR data for land-cover/land-use classification. The per-pixel method consisted of a maximum likelihood classification of fused data based on discrete wavelet transform and a classification from optical images alone. Optical and SAR data were then integrated into an object-oriented environment with the addition of texture measurements from SAR and classified with a nearest neighbor approach. Results were compared with the classification of the GeoEye-1 data alone and the outcomes pointed out that per-pixel data fusion did not improve the classification accuracy, while the object-based data integration increased the overall accuracy from 73% to 89%. According to results, an object-based approach with the introduction of adjunctive information layers proved to be more performing in land-cover/land-use classification than standard pixel-based methods.
2012 IEEE International Geoscience And Remote Sensing Symposium (IGARSS)
978-1-4673-1159-5
COSMO-SkyMed; GeoEye-1; OBIA; Data integration; Land-cover/land-use
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/686917
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