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Combining multiscale features for classification of hyperspectral images

Computer Science > Computer Vision and Pattern Recognition

Title: Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach

(Submitted on 15 Jun 2016)

Abstract: Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vector-based kernel is actually a special case of this kernel. Experiments conducted on various publicly available hyperspectral datasets illustrate the improvement of the proposed kernel w.r.t. conventional ones using the same hierarchical spatial features.

Comments: 8th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016), UCLA in Los Angeles, California, U.S
Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1606.04985 [cs.CV]
(or arXiv:1606.04985v1 [cs.CV] for this version)

Submission history

From: Yanwei Cui [view email]
[v1] Wed, 15 Jun 2016 21:19:54 GMT (972kb,D)

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