The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4/W6-2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-179-2023
https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-179-2023
07 Feb 2023
 | 07 Feb 2023

HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)

A. Jamali, M. Mahdianpari, and A. Abdul Rahman

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.