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Principal component analysis (PCA) is a multivariate statistical technique to summarize most of the variation in a observed multivariate system in fewer variables. It has been expected to be an useful technique to visualize the multiband remote sensing data. It is because the RGB color composite image using the summarized three principal components is expected to reflect as much information as possible of the original multivariate remote sensing data. It is known, however, that the actual color composite image employing PCA is not necessarily understandable one because the principal components are not distinctly correspondent to the electromagnetic wavelength, i. e. color, and consequently the relationship between the represented color and land cover becomes vague. This paper proposes a new type of the color composite technique by integrating PCA and the Normal Varimax Rotation (NVR) . The NVR is employed to make more distinct the relationship between each principal component and the electromagnetic wavelength by rotating the three dimensional space constituted by the first to third principal components. Since the NVR gives the orthogonal rotation, the three components after rotation represent the whole information of the original three principal components. This paper also presents the practical comparisons among the color composite images which are constructed by the conventional false color composite, the ordinary PCA, and the proposed NVR integrated PCA technique.

Color Composite of Satellite Remotely Sensed Image Integrating Principal Component Analysis and Normal Varimax Rotation.