研究目的
To propose a novel regularized nonnegative matrix factorization method for spectral-spatial dimension reduction of hyperspectral imagery that preserves geometric structure information and utilizes complementary features without additional parameters.
研究成果
The proposed NMFAGR method effectively performs dimension reduction by simultaneously learning low-dimensional representations and graph structures, automatically weighting features without extra parameters, and outperforms existing methods in classification accuracy on benchmark datasets, demonstrating its superiority for hyperspectral image processing.
研究不足
The method's performance may depend on the quality of the hyperspectral data and the choice of features; it requires computational resources for optimization, and the generalization to other datasets or features is not fully explored.
1：Experimental Design and Method Selection:
The study employs a novel NMF method with adaptive graph regularizer (NMFAGR) for dimension reduction, integrating graph learning and feature weighting. It uses an optimization algorithm based on the Optimal Gradient Method (OGM).
2：Sample Selection and Data Sources:
Three public hyperspectral datasets are used: Indian Pines, University of Pavia, and Pavia Centre, with samples split into training and test sets (10 samples per class for training).
3：List of Experimental Equipment and Materials:
No specific equipment mentioned; computational tools include MATLAB R2017a on a personal computer with Intel i7-6700 processor and 16 GB RAM.
4：Experimental Procedures and Operational Workflow:
Extract spectral, texture, and morphological features from hyperspectral images; apply dimension reduction methods; use SVM classifier for classification; compare with state-of-the-art methods (PCA, NPE, LPP, MFC, MONMF).
5：Data Analysis Methods:
Evaluate performance using overall accuracy (OA) and kappa coefficient; analyze parameter sensitivity and computational time.
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