Hierarchical Discriminative Feature Learning for Hyperspectral Image Classification

Building effective image representations from hyperspectral data helps to improve the performance for classification. In this letter, we develop a hierarchical discriminative feature learning algorithm for hyperspectral image classification, which is a deformation of the spatial-pyramid-matching mod...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 13; číslo 4; s. 594 - 598
Hlavní autoři: Xiangrong Zhang, Yunlong Liang, Yaoguo Zheng, Jinliang An, Jiao, L. C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Classification
Dictionaries
Discriminative feature learning
Feature extraction
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Image reconstruction
label-consistent K-SVD
Learning
Learning systems
Pine
Representations
Sparse matrices
sparse representation (SR)
State of the art
sparse representation (SR)
label-consistent K-SVD
Discriminative feature learning
hyperspectral image (HSI) classification
ISSN:1545-598X, 1558-0571
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Shrnutí:Building effective image representations from hyperspectral data helps to improve the performance for classification. In this letter, we develop a hierarchical discriminative feature learning algorithm for hyperspectral image classification, which is a deformation of the spatial-pyramid-matching model based on the sparse codes learned from the discriminative dictionary in each layer of a two-layer hierarchical scheme. The pooling features achieved by the proposed method are more robust and discriminative for the classification. We evaluate the proposed method on two hyperspectral data sets: Indiana Pines and Salinas scene. The results show our method possessing state-of-the-art classification accuracy.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2016.2528883