@inproceedings{db376ca8c48d40cb9c7212abd1db6e00,
title = "Relevant and invariant feature selection of hyperspectral images for domain generalization",
abstract = "This paper presents a novel feature selection method for the analysis of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant for the considered problem (i.e., preserve the functional relationship between input and output variables), and 2) invariant (stable) across different domains (i.e., minimize the data set shift among different domains). Domains can be associated with images collected on different areas or on the same area at different times. We propose a novel measure of domain stability, which evaluates the distance of the conditional distributions between the source and target domain. Such a measure is defined on the basis of kernel embeddings of conditional distributions and can be applied to both classification and regression problems. Experimental results show the effectiveness of the proposed method in selecting features with high generalization capabilities on the target domain.",
keywords = "Feature selection, Hyperspectral data, Image classification, Kernel methods, Remote sensing",
author = "Claudio Persello and Lorenzo Bruzzone",
year = "2014",
month = nov,
day = "4",
doi = "10.1109/IGARSS.2014.6947252",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "3562--3565",
booktitle = "International Geoscience and Remote Sensing Symposium (IGARSS)",
address = "United States",
note = "Joint International Geoscience and Remote Sensing Symposium (IGARSS 2014) / 35th Canadian Symposium on Remote Sensing (35th CSRS) : Energy and our Changing Planet, IGARSS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",
}