Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study


TÜRKER M., Ozdarici A.

INTERNATIONAL JOURNAL OF REMOTE SENSING, cilt.32, ss.9735-9768, 2011 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 32 Konu: 24
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1080/01431161.2011.576710
  • Dergi Adı: INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Sayfa Sayıları: ss.9735-9768

Özet

A comparison of agricultural crop maps from independent field-based classifications of the Satellite Pour l'Observation de la Terre (SPOT) 4 multispectral (XS), SPOT5 XS, IKONOS XS, QuickBird XS and QuickBird pan-sharpened (PS) images is presented. An agricultural area within the north-west section of Turkey was analysed for field-based crop identification. The SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird images were collected in similar climatic conditions during July and August 2004. The classification of each image was carried out separately on a per-field basis on all bands and the coincident bands that are green, red and near-infrared (NIR). To examine the effect of filtering on field-based classification, the images were each filtered using the 3 x 3, 5 x 5, 7 x 7 and 9 x 9 mean filter and the filtered bands were also classified on per-field basis. For the unfiltered images, IKONOS XS provided the highest overall accuracies of 88.9% and 88.1% for the all-bands and the coincident bands classifications, respectively. On average, IKONOS XS performed slightly better than QuickBird XS and QuickBird PS, while it outperformed SPOT4 XS and SPOT5 XS. The use of filtered images in field-based classification reduced the accuracies for SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird XS. The results of this study indicate that smoothing images prior to classification does not improve the accuracies for the field-based classification. On the contrary, the accuracies for the filtered QuickBird PS images indicated a slight improvement. On the whole, both IKONOS and QuickBird images produced quite promising results for field-based crop mapping, yielding overall accuracies above 83%.