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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization
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Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization

机译：多通道脉冲耦合神经网络的高光谱图像可视化

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摘要

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.
机译：高光谱图像（HSI）可视化，其旨在在具有自然色的三色显示器上显示原始图像的最佳图像的材料信息，在图像解释和分析中起着重要作用。然而，大多数HSI可视化方法仅关注呈现场景的详细信息而不提供自然色彩，并以类似的颜色区分土地覆盖物。为了解决这个问题，本文提出了一种多声道脉冲耦合神经网络（MPCNN）的基于HSI可视化方法，其包括以下步骤。首先，提出并探索MPCNN以融合原始的HSI，以便获得具有丰富空间细节的融合带。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RGB通道堆叠在一起以进行可视化。在四个高光谱数据集上执行的实验表明，所提出的方法不仅以自然颜色显示HSI，而且还改善了图像中的细节。在视觉效果和目标指标方面证明了所提出的方法的有效性。

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