Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes.

中文翻译：

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基于多通道脉冲耦合神经网络的高光谱图像可视化

高光谱图像（HSI）可视化旨在在具有自然色彩的三基色显示器上尽可能多地显示原始图像的物质信息，在图像解释和分析中起着重要作用。然而，大多数 HSI 可视化方法只专注于呈现场景的细节信息，而没有提供自然色彩和区分具有相似颜色的土地覆盖。为了解决这个问题，本文提出了一种基于多通道脉冲耦合神经网络（MPCNN）的HSI可视化方法，包括以下步骤。首先，提出并探索了 MPCNN 来融合原始 HSI，以获得具有丰富空间细节的融合波段。然后，提出了一种颜色映射方案来确定红色、绿色和蓝色 (RGB) 通道的权重。最后，加权的 RGB 通道堆叠在一起以进行可视化。在四个高光谱数据集上进行的实验表明，所提出的方法不仅显示具有自然色彩的 HSI，而且还改善了图像中的细节。从视觉效果和客观指标两个方面都证明了所提方法的有效性。