A new and effective method of NPPNMF (neighborhood preserving projective nonnegative matrix factorization) feature extraction for hyperspectral image classification

作者：Wen J^*; Tian Z; Lin W; Zhou M; Yan W

来源：Journal of Northwestern Polytechnical University, 2012, 30(1): 138-144.

• Analysis
• Classification (of information)
• Data mining
• Dimensionality reduction
• Efficiency
• Experiments
• Feature extraction
• Hyperspectral image
• Image analysis
• Image processing
• Matrix algebra
• NPPNMF (Neighborhood Preserving Projective Nonnegative Matrix Factorization)
• Pattern recognition

Aim. The introduction of the full paper reviews relevant matters and then proposes the NPPNMF feature extraction method mentioned in the title, which we believe is new and effective and which is explained in sections 1 through 3. Our new method incorporates the neighborhood preserving assumption. Section 1 briefs PNMF method. Section 2 explains our new NPPNMF method; it is divided into subsections 2.1 and 2.2. Section 3 analyzes the convergence of NPPNMF method; it gives the proof of four theorems. Section4 deals with experimental results and their analysis. Subsection 4.1 briefs AVIRIS hyperspectral data set. Subsection 4.3 presents experimental results on such data set in Figs. 2 and 3 and Table 1 and analyze these results. The theoretical analysis in section 3 and the analysis of experimental results in section 4 demonstrate preliminarily that the proposed new method is effective and promising in hyperspectral image classification.

• 出版日期2012
• 单位西北工业大学