Hyperspectral image classification based on spectral feature extraction

Li Wang; Wei Wang

doi:10.1117/12.2681034

8 June 2023 Hyperspectral image classification based on spectral feature extraction

Li Wang, Wei Wang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071B (2023) https://doi.org/10.1117/12.2681034
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Hyperspectral remote sensing can synchronously obtain the surface coverage space image and continuous spectral data, and can realize fine classification and recognition of ground objects. The motivation for this paper is to search an effective specteal feature extraction method for hyperspectral classification. The main methods used to extract spectral features of hyperspectral images are principal component analysis (PCA) and linear discriminant analysis (LDA). The realization of classification based on k-nearest neighbor (KNN) classifier is discussed. The training set and test set of Pavia University, Indian Pines and Salinas are selected as the data source, and the selection experiment of algorithm parameters is set, and the classification performance under different feature extraction methods is compared and analyzed. The experimental results show that with the parameters of OA, PA and Kappa based on the confusion matrix as the evaluation indicators, the classification accuracy based on LDA method is higher, and the effective classification of ground objects can be achieved. The results could provide a basis for further research on hyperspectral image classification based on spatial spectral feature extraction.

Citation Download Citation

Li Wang and Wei Wang "Hyperspectral image classification based on spectral feature extraction", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071B (8 June 2023); https://doi.org/10.1117/12.2681034

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