## 1 Introduction  
Hyperspectral images (HSIs) have recently become one of the most vital data sources for various computer vision tasks, such as target detection, anomaly detection, land surface classification, and disaster early warning. Three kinds of spaces are generally used in describing and processing HSIs: image space, spectral space, and feature space. For the human visual system, the most natural and intuitive way to express information is in the image space. Furthermore, the mainstream information display methods applied in practical applications today use the image space. Color display technology can intuitively present feature information of the spatial distribution of a HSI and can be very important for both scientific decision-making and information utilization. Raw HSI has abundant bands, so it cannot be directly used in trichromatic display, which is commonly used for traditional color images. Therefore, a common method for displaying HSI is to map the spectral information and spatial information on three color channels, such as RGB (red, green, and blue) and HSV (hue, saturation, and value) color space. While this display model transforms high-dimensional data into low-dimensional space, parts of the spectrum information will inevitably be lost. Currently, common HSI color visualization methods are based on the three approaches discussed below. The most straightforward approach for displaying hyperspectral images is directly selecting part of the data from HSI to display, such as selecting three bands based on some rules for a false color composite via RGB color space channels [1]. However, these methods only extract and display three bands and inevitably lose much useful information. Another approach is to directly process the raw hyperspectral data, condense the information into three channels, and then map the three channels into color space. This method can be carried out by the following methods: (1) use a simple data transformation [2]; (2) construct fixed linear spectral weighting envelopes, as proposed by [PERSON] and [PERSON] [3]; (3) apply dimensionality reduction based on matrix transformation [2; 3; 4; 5]; (4) apply a fusion method [6; 7]; (5) apply optimization methods [8; 9]; and (6) apply a machine learning method [10; 11]. These methods are widely used in hyperspectral visualization, but they still have some problems, such as producing dim images, a lack of clarity in the ranking of the significance of different channels, nonlinearity for real data, and computational demands [2; 3]. Moreover, one cannot take full advantage of supervised information, such as classification information. A third way to display hyperspectral images is to visualize pixel analysis results [2; 12; 13]. Data processing can improve color display, whereas use of classification approaches has higher class separability than the use of transformation approaches for the produced color display with concomitant complex implementation [2]. However, existing methods based on pixel analysis results destroy the globality of HSIs, and they are only applicable with a few classes (number of classes, \(n<6\)). Furthermore, hard classification-oriented methods are not suitable for displaying mixed pixels [13]. In addition, these methods do not use brightness information in the visualization. They not only reduce the information expressed in the image but also weaken the spatial distribution information of ground objects reflected in the picture. The methods mentioned above, which directly process raw hyperspectral data, can be considered as data-oriented approaches. These approaches are either unable to retain specific information for a specific object or they will destroy the globality of HSIs. Especially when supervised information is available for HSI data, the supervised information cannot be fully utilized with data-oriented approaches.