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Study on Improving Hyperspectral Target Detection by Target Signal Exclusion in Matched Filtering

초분광 영상의 표적신호 분리에 의한 Matched Filter의 표적물질 탐지 성능 향상 연구

  • Received : 2015.09.10
  • Accepted : 2015.09.30
  • Published : 2015.10.31

Abstract

In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.

본 연구에서는 초분광영상을 이용한 표적탐지에 있어 배경 신호 특징에 포함되는 표적 신호가 탐지성능에 미치는 영향을 살펴보고, 분광각을 기준으로 표적과 유사한 분광반사 특성을 가지는 화소들을 배경 특징화 과정에서 제외함으로써 표적탐지 성능을 향상시킬 수 있는 방법을 제안하였다. 초분광 표적탐지를 위해 가장 흔히 이용되는 matched Filter와 adaptive cosine estimator 기법에 대해 실제 항공 초분광영상 자료와 여기에 인공표적을 삽입하여 생성한 모의 자료를 이용한 실험 결과, 배경 특징화를 위한 공분산행렬 계산 시 표적 스펙트럼과 유사도가 높은 표적 유사화소들을 제외함으로써 탐지 성능이 크게 향상될 수 있음이 확인되었다. 분광각외에 다양한 유사도 판정 기준들에 대한 적용성 연구와 함께, 제외되는 표적 유사화소들의 양이 최적으로 결정될 수 있는 방법에 대한 추가 연구가 이루어진다면 사용이 간편하고 성능이 우수한 초분광 표적탐지 기법으로 활용될 수 있을 것으로 기대된다.

Keywords

References

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