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Bibliothèque Land cover classification using CHRIS/PROBA images and multi-temporal texture

Land cover classification using CHRIS/PROBA images and multi-temporal texture

Land cover classification using CHRIS/PROBA images and multi-temporal texture

Resource information

Date of publication
Décembre 2012
Resource Language
ISBN / Resource ID
AGRIS:US201400107031
Pages
101-119

Most existing multi-temporal classification studies use spectral information alone and ignore the temporal correlation between two-date images. This article proposes a new method to characterize the local temporal correlation using multi-temporal texture measured with a geostatistical function called the pseudo cross variogram (PCV). The derived multi-temporal texture, as an additional band, was combined with the spectral information in multi-temporal classification. The performance of the multi-temporal texture was evaluated and compared with the use of multi-temporal spectral data alone and plus the traditional variogram texture in land cover classification using bitemporal hyperspectral Compact High Resolution Imaging Spectrometer/Project for On Board Autonomy (CHRIS/PROBA) images. The results show that although land cover classification using spectral information from bitemporal CHRIS/PROBA data alone had an acceptable overall accuracy of 85.66%, the inclusion of multi-temporal texture in land cover classification led to significant increases (at the 95% confidence level) in both overall accuracy (3.3–4.3% improvement) and the kappa coefficient (4.9–6.6% improvement), particularly for vegetation classes. The incorporation of multi-temporal texture into multi-temporal land cover classification also outperformed the incorporation of the traditional variogram texture. The proposed method provides a new way to exploit the temporal correlation between bitemporal images for improved land cover classification.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Jin, Huiran
Li, Peijun
Cheng, Tao
Song, Benqin

Publisher(s)
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