Abstract:

A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final superpixel segmentation. The proposed method is applied to two real hyperspectral data sets and quantitative cluster validity metrics indicate that the proposed approach outperforms existing hyperspectral superpixel segmentation methods.

Links:

Citation:

H. Sun and A. Zare, “Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps” Proc. IEEE Intl. Geosci. Remote Sens. Symp. (IGARSS), Fort Worth, TX, 2017. 

@InProceedings{sun2017map,
author = {Sun, H. and Zare, A.},
title = {Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps},
booktitle = {Proc. IEEE Intl. Geosci. Remote Sens. Symp.},
year = {2017}
}