Hybrid Dimensionality Reduction Technique for Hyperspectral Images Using Random Projection and Manifold Learning
| Autor: | Alkha Mohan, M. Venkatesan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
business.industry Computer science Random projection Dimensionality reduction 0211 other engineering and technologies Nonlinear dimensionality reduction Hyperspectral imaging Pattern recognition 02 engineering and technology 01 natural sciences Kernel principal component analysis Reduction (complexity) Nonlinear system Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business 021101 geological & geomatics engineering 0105 earth and related environmental sciences Curse of dimensionality |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030604691 |
| DOI: | 10.1007/978-3-030-60470-7_12 |
| Popis: | Hyperspectral images (HSI) are contiguous band images having hundreds of bands. However, most of the bands are redundant and irrelevant. Curse of dimensionality is a significant problem in hyperspectral image analysis. The band extraction technique is one of the dimensionality reduction (DR) method applicable in HSI. Linear dimensionality reduction techniques fail for hyperspectral images due to its nonlinearity nature. Nonlinear reduction techniques are computationally complex. Therefore this paper introduces a hybrid dimensionality reduction technique for band extraction in hyperspectral images. It is a combination of linear random projection (RP) and nonlinear technique. The random projection method reduces the dimensionality of hyperspectral images linearly using either Gaussian or Sparse distribution matrix. Sparse random projection (SRP) is computationally less complex. This reduced image is fed into a nonlinear technique and performs band extraction in minimal computational time and maximum classification accuracy. For experimental analysis of the proposed method, the hybrid technique is compared with Kernel PCA (KPCA) using different random matrix and found a promising improvement in results for their hybrid models in minimum computation time than classic nonlinear technique. |
| Databáze: | OpenAIRE |
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