A Feature-Metric-Based Affinity Propagation Technique for Feature Selection in Hyperspectral Image Classification

作者：Yang, Chen^*; Liu, Sicong; Bruzzone, Lorenzo; Guan, Renchu; Du, Peijun

来源：IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1152-1156.

DOI：10.1109/LGRS.2012.2233711

• Affinity propagation (AP)
• feature metric (FM)
• feature selection
• hyperspectral images
• relevant component analysis (RCA)

Relevant component analysis has shown effective in metric learning. It finds a transformation matrix of the feature space using equivalence constraints. This paper explores this idea for constructing a feature metric (FM) and develops a novel semisupervised feature-selection technique for hyperspectral image classification. Two feature measures referred to as band correlation metric (BCM) and band separability metric (BSM) are derived for the FM. The BCM can measure the spectral correlation among the bands, while the BSM can assess the class discrimination capability of a single band. The proposed feature-metric-based affinity propagation (AP) (FM-AP) technique utilizes exemplar-based clustering, i.e., AP, to group bands from original spectral channels with the FM. Experimental results are conducted on two hyperspectral images and show the advantages of the proposed technique over traditional feature-selection methods.

• 出版日期2013-9
• 单位吉林大学; 南京大学