Details

Autor(en) / Beteiligte

• Xiangrong Zhang
• Yunlong Liang
• Yaoguo Zheng
• Jinliang An
• Jiao, L. C.

Titel

Hierarchical Discriminative Feature Learning for Hyperspectral Image Classification

Ist Teil von

• IEEE geoscience and remote sensing letters, 2016-04, Vol.13 (4), p.594-598

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2016

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• 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.