Leveraging distributed and parallel algorithm for normalized PCA in hyperspectral image analysis

• 발행기관: 한국자료분석학회
• 저자명: Sang-Hoon Cho
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.26 No.6, 1649~1659쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

In recent years, the availability of hyperspectral data has rapidly expanded across diverse fields of study. These data, measured by hundreds of spectral bands, are characterized by high dimensionality, posing significant challenges for analysis. Principal Component Analysis (PCA) is a widely-used method for dimensionality reduction. However, sample principal components (PCs) are generally sensitive to difference in scale, and thus hyperspectral datasets with varying scales require proper normalization. Furthermore, the massive volume of hyperspectral data necessitates adopting distributed and parallel computing platforms. In this article, we propose a single-phase MapReduce-based algorithm for normalized PCA on large-scale high-dimensional hyperspectral data by leveraging the distributed and parallel architecture of RHadoop. Our experiment on the Indian Pines dataset underscores the substantial impact of the normalization process on PCA-based feature selection and subsequent machine learning analyses. Applying our proposed algorithm to the large-scale HySpecNet-11k benchmark dataset validates its scalability and efficiency.

목차

1. Introduction

2. Normalization Techniques for Hyperspectral PCA

3. Single-Phase MapReduce Algorithm for Normalized PCA

4. Normalization Effects on PCA-based Machine Learning Performance

5. Scalability Testing with HySpecNet-11k: a Large-Scale Case Study

6. Conclusion

References

키워드

Hyperspectral Image Analysis Normalization Principal Component Analysis MapReduce Algorithm Dimensionality Reduction

참고문헌

• arXiv preprint
• IEEE Transactions on Geoscience and Remote Sensing
• Proceedings of the 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
• Journal of the Korean Data Analysis Society
• Journal of the Korean Data Analysis Society
• Journal of the Korean Data Analysis Society
• International Journal of Electrical and Computer Engineering
• Journal of the Korean Data Analysis Society
• Journal of the Korean Institute of Communications and Information Sciences
• Journal of the Korean Data Analysis Society
• Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
• IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium
• Journal of the Korean Data Analysis Society
• IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing