@inproceedings{4b175fafd7e84a4a8a52ef8f8374544a,
title = "Object based fusion of polarimetric SAR and hyperspectral imaging for land use classification",
abstract = "In this paper, we propose an object-based fusion approach for the joint use of polarimetric synthetic aperture radar (PolSAR) and hyperspectral data. The proposed approach extracts information from both datasets based on an object-level, which is used here for land use classification. The achieved classification result infers that the proposed methodology improves the classification performance of both hyperspectral and PolSAR data and can properly gather complementary information of the two kinds of dataset. The fusion approach also considers that only limited training samples are available, which is often the case in remote sensing.",
keywords = "Classification, Fusion, Hyperspectral image, PolSAR",
author = "Jingliang Hu and Pedram Ghamisi and Andreas Schmitt and Zhu, {Xiao Xiang}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 ; Conference date: 21-08-2016 Through 24-08-2016",
year = "2016",
month = jun,
day = "28",
doi = "10.1109/WHISPERS.2016.8071752",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2016 8th Workshop on Hyperspectral Image and Signal Processing",
}
