@inproceedings{d90aaaf8826748d1ad95d5010e8bbd94,
title = "Principal polynomial analysis for remote sensing data processing",
abstract = "PCA is widely used in this context but its linear features are optimal in error reconstruction terms only in the case that data displays a particular symmetry which is not always guaranteed in remote sensing. The proposed PPA is a non-linear generalization of PCA that relaxes the required symmetry of the data. We analytically proved that PPA always improves PCA performance in reconstruction error after dimensionality reduction as well as in energy compaction. Results on high resolution multispectral images show the suitability of using PPA for remote sensing data analysis.",
author = "V. Laparra and D. Tuia and S. Jim{\'e}nez and G. Camps-Valls and J. Malo",
year = "2011",
month = nov,
day = "16",
doi = "10.1109/IGARSS.2011.6050151",
language = "English",
isbn = "9781457710056",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
pages = "4180--4183",
booktitle = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings",
note = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 ; Conference date: 24-07-2011 Through 29-07-2011",
}