@inproceedings{9de3efbd0cdf4515a6688b0f5f3a156b,
title = "Spectral-spatial classification of hyperspectral image based on locality preserving discriminant analysis",
abstract = "In this paper, a spectral-spatial classification method for hyperspectral image based on spatial filtering and feature extraction is proposed. To extract the spatial information that contain spatially homogeneous property and distinct boundary, the original hyperspectral image is processed by an improved bilateral filter firstly. And then the proposed feature extraction algorithm called locality preserving discriminant analysis, which can explore the manifold structure and intrinsic characteristics of the hyperspectral dataset, is used to reduce the dimensionality of both the spectral and spatial features. Finally, a support vector machine with a composite kernel is used to examine the performance of the proposed methods. Experiments results on a hyperspectral dataset demonstrate the effectiveness of the proposed algorithm in the classification tasks.",
keywords = "Feature extraction, Hyperspectral, Manifold structure, Spatial filtering, Support vector machine with a composite kernel",
author = "Min Han and Chengkun Zhang and Jun Wang",
year = "2016",
doi = "10.1007/978-3-319-40663-3\_3",
language = "English",
isbn = "9783319406626",
volume = "9719",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "21--29",
editor = "Long Cheng and Qingshan Liu and Andrey Ronzhin",
booktitle = "Advances in Neural Networks",
address = "Germany",
note = "13th International Symposium on Neural Networks, ISNN 2016 ; Conference date: 06-07-2016 Through 08-07-2016",
}