Abstract
In this paper, we will introduce a new method to investigate the best visualization of remote sensing imagery data sets on graphics processing units (GPU). The main shortcoming of classical dimension reduction methods is that their visualizations inevitably lose a significant amount of information in remote sensing imagery. Our proposed method (Enhancement Trustworthy (ET)) preserves the color distance as noticeably as possible for high dimension space by defining parallel-supervised extra step to enhance the quality of visualization. The ET classifies the visualization colors into false and true, and constructs a trustworthiness of CIE color space to improve the trustworthy value of false colors. The trustworthiness value of points inside CIE color space illustrate the locations of false and true projected colors in the visualization. Our method is supervised, where the user can select the interested points to enhance by ET. The proposed method is general enhancement step which can be used to enhance any visualization, for example, we used it to enhance the visualizations of remote sensing imagery data sets, which are carried by PCA, SPE and Isomap methods. The results showed the ability of ET to overcome the false colors and return the more reliable true colors. The colors of enhance visualizations showed more details which represent the real neighborhood relation in original data sets.
Published Version
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