HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)
Keywords: LULC Mapping, Big data, Hyperspectral, Image Classification, Machine Learning, Multi-layer Perceptron
Abstract. The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophisticated Multi-Layer Perceptron (MLP) algorithms. In this paper, we propose using MLP-Mixer to classify HSI data in three data benchmarks of Pavia, Salinas, and Indian Pines. Based on the results, the proposed MLP-Mixer achieved a high level of classification accuracy and produced noise-free and homogenous classification maps in all study areas. For the classification of HSI data in Salinas, Indian Pines, and Pavia, the proposed MLP-Mixer achieved an average accuracy of 99.82%, 99.81%, and 99.23%, respectively.