Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

Yuliya Tarabalka

doi:10.1109/TGRS.2010.2062526

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

2010-11-01

228

OA

AI

Y

Yuliya Tarabalka *

J

Jón Atli Benediktsson

DOI：10.1109/TGRS.2010.2062526

Original

Original request for help

Save

Abstract

En 中文

A new multiple-classifier approach for spectralspatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region with a corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker-selection procedure, each of them combining the results of a pixelwise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectral-spatial classification map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies when compared with previously proposed classification techniques.

Keywords:

Classification

hyperspectral images

minimum spanning forest (MSF)

multiple classifiers (MCs)

segmentation

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

Abstract

Journal

Organization