Publisher's version (útgefin grein)Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability () in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and, which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).This research was funded by the National Natural Science Foundation of China, grant numbers 61275010 and 61675051. The authors would like to thank D. Landgrebe from Purdue University for providing the AVIRIS Indian Pines data set and Prof. P. Gamba from the University of Pavia for providing the ROSIS-3 University of Pavia data set. The authors would like to express their appreciation to Jon Qiaosen Chen from the University of Iceland and Di Chen for helping improve the language of the paper.Peer Reviewe
