Abstract: Aiming at the problem of huge amount of data, strong data correlation, and integration of maps and spectrum in hyperspectral images, which might cause difficulty to classify hyperspectral images, a hyperspectral image classification model based on multi-classifier fusion is constructed. The model first uses bilateral filtering algorithm for denoising, and then uses the combination of LDA algorithm and PCA algorithm, separate PCA algorithm, and Gabor filter and PCA algorithm to perform dimensionality reduction and feature extraction on the data respectively, and use SVM classifier, LightGBM classifier and AdaBoost classifier for classification. Finally, an AHP-voting method is designed to merge the classification results of the three classifiers. The results show that the effect of the fusion model is significantly increased, the overall accuracy (OA) up to 97.59%, the average accuracy (AA) up to 98.95%, Kappa coefficient can reach more than 97.32%, the OA, AA, and Kappa coefficients are improved by 2.30%, 1.13%, and 2.54% on average compared with a single model classifier.

Key words: hyperspectral image classification, Gabor filtering, LightGBM, AdaBoost, multi classifier fusion