This paper presents a composite kernel method (MWASCK) based on multiscale weighted adjacent superpixels (ASs) to classify hyperspectral image (HSI). The MWASCK adequately exploits spatial-spectral features of weighted adjacent superpixels to guarantee that more accurate spectral features can be extracted. Firstly, we use a superpixel segmentation algorithm to divide HSI into multiple superpixels. Secondly, the similarities between each target superpixel and its ASs are calculated to construct the spatial features. Finally, a weighted AS-based composite kernel (WASCK) method for HSI classification is proposed. In order to avoid seeking for the optimal superpixel scale and fuse the multiscale spatial features, the MWASCK method uses multiscale weighted superpixel neighbor information. Experiments from two real HSIs indicate that superior performance of the WASCK and MWASCK methods compared with some popular classification methods.

中文翻译：

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用于超光谱图像分类的多尺度加权相邻超像素复合核

本文提出了一种基于多尺度加权相邻超像素（AS）的复合核方法（MWASCK）对高光谱图像（HSI）进行分类。MWASCK充分利用了加权相邻超像素的空间光谱特征，以确保可以提取出更准确的光谱特征。首先，我们使用超像素分割算法将HSI划分为多个超像素。其次，计算每个目标超像素与其AS之间的相似度以构造空间特征。最后，提出了一种基于加权的基于AS的复合核（WASCK）用于HSI分类。为了避免寻找最佳的超像素尺度并融合多尺度空间特征，MWASCK方法使用了多尺度加权超像素邻居信息。