KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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A Study on the Optimum Band Selection for Supervised Classification Using Mixed-Pixels and Canonical Correlation Analysis

혼합화소와 정준상관분석을 이용한 감독분류의 최적밴드선정에 관한 연구

Chang, Hoon;Yoon, Wan-Seok;Shin, Dong-June
장훈;윤완석;신동준

  • Published : 2004.11.30

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Abstract

The commonly used methods for the optimum band selection for supervised classification of multispectral data are Divergence, Transformed divergence (TD) and Jeffreys-Matusita distance(JM distance). But those methods have several problems and are ineffective. This study introduces cost-effective algorithm for feature selection. With 'bands variables set' that includes the pixel values and 'classes variables set' that includes the membership of each class in the pixel, the canonical correlation analysis is made use of feature selection. Using the canonical cross-loadings we can orderly identify the bands that largely influence the remotely sensed data. to verify the suitability of the new algorithm, the classifications using the each best band combination through TD, JM distance and new method were performed and the accuracy assessment was performed. As a result of classification accuracy assessment, overall accuracy and k^ for the new method were higher than TD's and had competitive results to JM distance method.

감독분류 수행을 위해 최적밴드조합을 선정하는 기존의 대표적인 방법인 Divergence, Transformed Divergence, Jeffreys-Matusita Distance등의 알고리즘들은 여러 가지 문제점을 안고 있으며 비효율적이다. 본 논문에서는 이에 대해 효율적인 알고리즘을 제시하였다. 밴드들의 픽셀값을 나타내는 밴드 변수군과 클래스들의 혼합화소 비율을 나타내는 클래스 변수군에 대해 전체 픽셀을 대상으로 정준상관분석을 수행함으로써 클래스 변수군에 대한 밴드변수들의 정준교차적재를 이용하여 최적밴드조합을 선정하였다. 본 논문에서 제시한 알고리즘의 효율성을 평가하기 위해서 결과로 나온 최적밴드조합을 이용하여 분류를 수행한 결과 분류정확도가 Transformed divergence보다 다소 높게 나왔으며, JM distance보다 약간 낮은 값을 나타냈지만 비슷한 결과를 보였다.

Keywords

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