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Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings

Abstract : We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions - by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate each pixel to an empirical distribution of its neighbouring pixels, a judicious representation of which in an RKHS, in conjunction with the spectral information contained in the pixel itself, give a new explicit set of features that can be fed into a suite of standard classification techniques - we opt for a well established framework of support vector machines (SVM). Furthermore, the computational complexity is reduced via random Fourier features formalism. We study the consistency and the convergence rates of the proposed method and the experiments demonstrate strong performance on hyperspectral data with gains in comparison to the state-of-the-art results.
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Contributeur : Jesus Angulo Connectez-vous pour contacter le contributeur
Soumis le : jeudi 26 janvier 2017 - 14:59:08
Dernière modification le : samedi 22 octobre 2022 - 05:13:12

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Gianni Franchi, Jesus Angulo, Dino Sejdinovic. Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings. 2016 IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, United States. ⟨10.1109/ICIP.2016.7532688⟩. ⟨hal-01446988⟩



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