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

Huiran Jin, Peijun Li, Tao Cheng, Benqin Song

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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 vari-ogram (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.

Original languageEnglish (US)
Pages (from-to)101-119
Number of pages19
JournalInternational Journal of Remote Sensing
Volume33
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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