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

Source: [Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach](<https://www.sota2.com/research/paper/68fa62831e209131ce9f9c70>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:50:53.386Z

Authors: Yanwei Cui, Laetitia Chapel, S\\'ebastien Lef\\\`evre

Published: 2016-06-15T21:19:54.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1606.04985>)

[PDF](<https://arxiv.org/pdf/1606.04985.pdf>)