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Y. Hirosawa, S. E. Marsh and D. H. Kliman, “Application of Standardized Principal Component Analysis to LandCover Characterization Using Multitemporal AVHRR Data,” Remote Sensing of Environment, Vol. 58, No. 3, pp. 267-281. doi:10.1016/S0034-4257(96)00068-5

has been cited by the following article:

• TITLE: A Simplified Approach for Interpreting Principal Component Images

  AUTHORS: Ravi P. Gupta, Reet K. Tiwari, Varinder Saini, Neeraj Srivastava

  KEYWORDS: Principal Component Images; Remote Sensing; Eigenvectors; Spectral Behavior

  JOURNAL NAME: Advances in Remote Sensing, Vol.2 No.2, June 11, 2013

  ABSTRACT: Principal component transformation is a standard technique for multi-dimensional data analysis. The purpose of the present article is to elucidate the procedure for interpreting PC images. The discussion focuses on logically explaining how the negative/positive PC eigenvectors (loadings) in combination with strong reflection/absorption spectral behavior at different pixels affect the DN values in the output PC images. It is an explanatory article so that fuller potential of the PCT applications can be realized.