[A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood...

Area of Science:

• Remote Sensing
• Computer Vision
• Image Processing

Background:

• Traditional hyperspectral image classification primarily uses spectral information.
• Increasing spatial resolution reveals spatial clustering properties for similar categories.
• Directly incorporating spatial information without selection can increase errors and computation time.

Purpose of the Study:

• To develop a spatial-spectral joint sparse representation classification algorithm for hyperspectral images.
• To improve classification accuracy by effectively utilizing spatial information.
• To address the challenge of marginal differences between categories in hyperspectral data.

Main Methods:

• A novel spatial-spectral joint sparse representation classification algorithm based on neighborhood segmentation is proposed.
• Spectral angle similarity is used to select appropriate neighborhood pixels for the model.
• Simultaneous subspace pursuit and simultaneous orthogonal matching pursuit are employed to solve the model.
• Classification is determined by minimum reconstruction error between testing samples and training pixels.

Main Results:

• The proposed algorithm demonstrates improved classification accuracy on AVIRIS and ROSIS hyperspectral datasets.
• Classification accuracy consistently increases with the neighborhood segmentation threshold.
• The effectiveness of neighborhood segmentation in joint sparse representation classification is validated.

Conclusions:

• Neighborhood segmentation is a necessary step for effective joint sparse representation classification of hyperspectral images.
• The proposed algorithm offers a robust approach to enhance hyperspectral image classification accuracy.
• Spatial information, when selectively incorporated, significantly benefits hyperspectral image analysis.