Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

• Published in: IEEE transactions on geoscience and remote sensing Vol. 48; no. 11; pp. 4122 - 4132
• Main Authors: Tarabalka, Yuliya, Benediktsson, Jón Atli, Chanussot, Jocelyn, Tilton, James C
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Classification; Classifiers; Clustering algorithms; Computer Science; Forests; hyperspectral images; Hyperspectral imaging; Image Processing; Image segmentation; Markers; minimum spanning forest (MSF); multiple classifiers (MCs); Partitioning algorithms; Pixel; Pixels; Seeds; Segmentation; Spectra; Support vector machines; segmentation; hyperspectral images; minimum spanning forest (MSF); Classification; multiple classifiers (MCs)
• DOI: 10.1109/TGRS.2010.2062526
• ISSN: 0196-2892

A new multiple-classifier approach for spectral-spatial 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.