Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadatas

https://hal-mines-paristech.archives-ouvertes.fr/hal-01446988
Contributor : Jesus Angulo <>
Submitted on : Thursday, January 26, 2017 - 2:59:08 PM
Last modification on : Thursday, September 24, 2020 - 4:38:04 PM

Links full text

Identifiers

Citation

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⟩

Share

Metrics

Record views

316