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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Similarity-Based Multiple Kernel Learning Algorithms for Classification of Remotely Sensed Images
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Similarity-Based Multiple Kernel Learning Algorithms for Classification of Remotely Sensed Images

机译：基于相似度的多核学习算法在遥感图像分类中的应用

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摘要

Multiple kernel learning (MKL) algorithms are proposed to address the problems associated with kernel selection of the kernel-based classification algorithms. Using a group of kernels rather than one single kernel, the MKL algorithms aim to provide better classification efficiency. This paper presents new similarity-based MKL algorithms to classify remote-sensing images. These algorithms find the optimal combination of kernels by maximizing the similarity between a combination of kernels and an ideal kernel. In this framework, we initially introduced three similarity measures to be used: kernel alignment, norm of kernel difference, and Hilbert–Schmidt independence criterion. Then, we proposed to solve the optimization problems of the MKL algorithm associated with each similarity measure adopting heuristic and convex optimization methods. The performances of the proposed algorithms were compared with a single kernel support vector machines as well as other MKL algorithms for classifying the features extracted from the high-resolution and hyperspectral images. The results demonstrated that the similarity-based MKL algorithms performed better than other algorithms, especially when their optimization problems were solved using the convex optimization methods or when few training samples were available. Moreover, when the optimization problems of these algorithms were solved using the heuristic optimization methods, they were able to yield acceptable performances and were faster than other MKL algorithms.
机译：提出了多种内核学习（MKL）算法，以解决与基于内核的分类算法的内核选择相关的问题。 MKL算法使用一组内核而不是一个内核，旨在提供更好的分类效率。本文提出了新的基于相似度的MKL算法对遥感图像进行分类。这些算法通过最大化内核组合和理想内核之间的相似性来找到内核的最佳组合。在此框架中，我们最初引入了三种要使用的相似性度量：内核对齐，内核差异范数和Hilbert-Schmidt独立性准则。然后，我们提出采用启发式和凸优化方法来解决与每个相似性度量相关的MKL算法的优化问题。将所提出算法的性能与单核支持向量机以及其他MKL算法进行了比较，以对从高分辨率和高光谱图像中提取的特征进行分类。结果表明，基于相似度的MKL算法的性能优于其他算法，尤其是当使用凸优化方法解决了它们的优化问题或训练样本很少时。此外，当使用启发式优化方法解决这些算法的优化问题时，它们能够产生可接受的性能，并且比其他MKL算法更快。

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