@inproceedings{e164012e41384d0da01661e1c339342d,
title = "Tensor morphological profile for hyperspectral image classification",
abstract = "This paper proposes a novel multi-dimensional morphology descriptor, tensor morphology profile (TMP), for hyperspectral image classification. TMP is a general framework to extract the multi-dimensional structures in high-dimensional data. The nth-order morphology profile is proposed to work with the nth-order tensor, which can capture the inner high order structures. This is different with the traditional mathematical morphology operations which are usually limited to two-dimensional data. By treating hyperspectral images a tensor, it is possible to extend the morphology to high dimensional data so that the powerful morphological tools can be used to analyze the hyperspectral images with spectral-spatial information fused. Experimental results on two commonly used hyperspectral images show that the tensor morphological profile consistently performs better than the extended morphological profile for hyperspectral image classification.",
keywords = "Hyperspectral imaging classification, Mathematical morphology, Tensor modeling",
author = "Jie Liang and Jun Zhou and Yongsheng Gao",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",
year = "2016",
month = aug,
day = "3",
doi = "10.1109/ICIP.2016.7532748",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2197--2201",
booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",
address = "United States",
}