This research has proposed an algorithm for producing Color Composite Moving image for image feature enhancement using hyperspectral data (HS data) which consists of the following steps: Step 1) Through the principal component analysis for HS data, the orthogonal transform images from the first to the third principal components is produced, Step 2) as a measure for the issues of subjective determination on the range of contrast stretching, repeated contrast stretching is applied for those principal component images, Step 3) Color composite static image (termed “s-PCC image”) is produced by assigning the principal components to the R, G, and B planes, respectively. The combination case for assigning principal component images to RGB planes is determined based on the values of correlated color temperature and; Step 4) For visualizing the differences of the quality of s-PCC images during the contrast stretching process, an animation-like color composite moving image (termed “m-PCC image”) is produced by continuously displaying s-PCC images. The experimental results for image features on concrete surface indicate that all the information of image feature enhancement and coloration process on m-PCC image can be visualized dynamically and repeatedly, resulting in an unprecedented effectiveness for image feature enhancement and interpretation.