Refining Sparse Coding Dictionaries Using High Dimensional Model Representation for Hyperspectral Imagery

Authors : Süha Tuna

Pages : 121-132

Doi:10.34088/kojose.1658929

View : 54 | Download : 137

Publication Date : 2025-11-30

Article Type : Research Paper

Abstract :Hyperspectral imaging, known for its rich spectral and spatial details, finds applications in remote sensing, biomedical engineering, and quality control. Nonetheless, the high dimensionality and large data volume of hyperspectral images create substantial challenges in efficient processing and classification. Sparse coding-based techniques are widely employed to tackle these challenges. Therefore, an appropriate dictionary should be constructed to improve the efficacy of the sparse coding-based methods. This study introduces a dictionary refinement method that enhances sparse coding-based classification by exploiting an efficient denoising and decorrelation technique named High Dimensional Model Representation. This technique decomposes the 3-D hyperspectral data into manageable components, effectively reducing noise and correlations. Then, a refined dictionary is acquired by using random spectral signals of the hyperspectral data under consideration. The sparse coding-based classifier adopting the refined dictionary is exploited to improve the classification accuracy. Experimental results on widely used HS datasets show that the proposed method significantly boosts classification accuracy. This method leverages the benefits of denoising and decorrelation of the High Dimensional Model Representation method to generate a refined dictionary and provide a robust and efficient solution for hyperspectral image classification.
Keywords : Sınıflandırma, Sözlük İyileştirme, Yüksek Boyutlu Model Gösterilimi, Hiperspektral Görüntüleme, Makine Öğrenmesi, Optimizasyon, Seyrek Kodlama

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