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Accuracy Assessment of Supervised Classification using Training Samples Acquired by a Field Spectroradiometer: A Case Study for Kumnam-myun, Sejong City

지상 분광반사자료를 훈련샘플로 이용한 감독분류의 정확도 평가: 세종시 금남면을 사례로

  • Received : 2016.03.15
  • Accepted : 2016.03.28
  • Published : 2016.03.31

Abstract

Many studies are focused on image data and classifier for comparison or improvement of classification accuracy. Therefore studies are needed aspect of the training samples on supervised classification which depend on reference data or skill of analyst. This study tries to assess usability of field spectra as training samples on supervised classification. Classification accuracies of hyperspectral and multispectral images were assessed using training samples from image itself and field spectra, respectively. The results shown about 90% accuracy with training sample collected from image. Using field spectra as training sample, accuracy was decreased 10%p for hyperspectral image, and 20%p for multispectral image. Especially, some classes shown very low accuracies due to similar spectral characteristics on multispectral image. Therefore, field spectra might be used as training samples on classification of hyperspectral image, although it has limitation for multispectral image.

Keywords

Supervised Classification;Accuracy;Training Sample;Field Spectra

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Acknowledgement

Supported by : 국토교통부