Abstract. Hyperspectral image (HSI) analysis is a growing area in the community of remote sensing, particularly with images exhibiting high spatial and spectral resolutions. Multiple kernel learning (MKL) has been proposed and found to classify HSIs efficiently owing to its capability for handling diverse feature fusion. However, constructing base kernels, selecting key kernels, and adjusting their contributions to the final kernel remain major challenges for MKL. We propose a scheme to generate effective base kernels and optimize their weights, which represent their contribution to the final kernel. In addition, both spatial and spectral information are utilized to improve the classification accuracy. In the proposed scheme, the spatial features of HSIs are introduced through multiscale feature representations that preserve the relationship between the classification process and the pixel context. MKL and self-organizing maps (SOMs) are integrated and used for the unsupervised classification of HSIs. The weights of both the base kernels and neural networks are simultaneously optimized in an unsupervised manner. The results indicate that the proposed MKL-SOM scheme outperforms state-of-the-art algorithms, particularly when applied to large HSIs. Moreover, its ability to fuse multiscale features, especially in large HSIs, is useful for various analysis tasks.

中文翻译：

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基于多核自组织图的高光谱图像分类无监督光谱空间多尺度特征学习框架

摘要。高光谱图像 (HSI) 分析是遥感领域中一个不断增长的领域，尤其是具有高空间和光谱分辨率的图像。多核学习 (MKL) 已被提出并被发现可以有效地对 HSI 进行分类，因为它能够处理不同的特征融合。然而，构建基础内核、选择关键内核以及调整它们对最终内核的贡献仍然是 MKL 的主要挑战。我们提出了一种生成有效基础内核并优化它们的权重的方案，这些权重代表了它们对最终内核的贡献。此外，利用空间和光谱信息来提高分类精度。在提议的方案中，HSI 的空间特征是通过多尺度特征表示引入的，这些特征表示保留了分类过程和像素上下文之间的关系。MKL 和自组织映射 (SOM) 被集成并用于 HSI 的无监督分类。基础内核和神经网络的权重以无监督的方式同时优化。结果表明，所提出的 MKL-SOM 方案优于最先进的算法，尤其是在应用于大型 HSI 时。此外，它融合多尺度特征的能力，尤其是在大型 HSI 中，对于各种分析任务非常有用。基础内核和神经网络的权重以无监督的方式同时优化。结果表明，所提出的 MKL-SOM 方案优于最先进的算法，尤其是在应用于大型 HSI 时。此外，它融合多尺度特征的能力，尤其是在大型 HSI 中，对于各种分析任务非常有用。基础内核和神经网络的权重以无监督的方式同时优化。结果表明，所提出的 MKL-SOM 方案优于最先进的算法，尤其是在应用于大型 HSI 时。此外，它融合多尺度特征的能力，尤其是在大型 HSI 中，对于各种分析任务非常有用。