Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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MODIS Data-based Crop Classification using Selective Hierarchical Classification

선택적 계층 분류를 이용한 MODIS 자료 기반 작물 분류

  • Kim, Yeseul (Department of Geoinformatic Engineering, Inha University) ;
  • Lee, Kyung-Do (Climate Change and Agroecology Division, National Institute of Agricultural Sciences) ;
  • Na, Sang-Il (Climate Change and Agroecology Division, National Institute of Agricultural Sciences) ;
  • Hong, Suk-Young (Soil and Fertilizer Division, National Institute of Agricultural Sciences) ;
  • Park, No-Wook (Department of Geoinformatic Engineering, Inha University) ;
  • Yoo, Hee Young (Geoinformatic Engineering Research Institute, Inha University)
  • 김예슬 (인하대학교 공간정보공학과) ;
  • 이경도 (국립농업과학원 기후변화생태과) ;
  • 나상일 (국립농업과학원 기후변화생태과) ;
  • 홍석영 (국립농업과학원 토양비료과) ;
  • 박노욱 (인하대학교 공간정보공학과) ;
  • 유희영 (인하대학교 공간정보공학연구소)
  • Received : 2016.05.24
  • Accepted : 2016.06.08
  • Published : 2016.06.30
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Abstract

In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.

MODIS 자료를 이용한 대규모 작물 분류에는 MODIS 자료의 상대적으로 낮은 공간해상도로 인한 분광학적 혼재 양상이 두드러지게 나타난다. 이러한 분광학적 혼재를 완화하기 위하여 이 연구에서는 작물의 분광특성을 이용하여 특정 작물의 계층을 선택적으로 구분하고 상세 분류를 수행하는 선택적 계층 분류 방법론을 제안하였다. 제안 방법론에서는 특정 작물에 대한 선택적 분류를 수행함으로써 작물간의 혼재를 완화하고 구분력을 향상시킬 수 있다. 제안 방법론의 적용성 평가에는 중국 길림성의 길림시를 대상으로 MODIS 정규식생지수 자료와 근적외선 자료를 이용한 작물 분류의 사례 연구를 수행하였다. 먼저 근적외선 자료의 무감독 분류를 수행하여 벼의 재배지역을 우선적으로 추출한 후에, 시계열 정규식생지수 자료를 이용하여 벼 재배지역이 아닌 영역을 대상으로 옥수수와 콩의 상세 분류를 수행하였다. 사례 연구 결과, 제안 방법론은 유사한 분광특성을 갖는 작물의 계층을 선택적으로 구분함으로써 기존 시계열 정규식생지수 자료와 근적외선 자료를 함께 이용하는 감독 분류 결과보다 향상된 분류 정확도를 나타내었다. 따라서 신뢰성 있는 작물 구분도 제작에 제안 방법론이 효과적으로 사용될 수 있을 것으로 기대된다.

Keywords

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