Hyperspectral image classification using multi-layer perceptron mixer (MLP-MIXER).

• Main Authors: Jamali, Ali, Mahdianpari, Masoud, Abdul Rahman, Alias
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: TH434-437 Quantity surveying
• Online Access: http://eprints.utm.my/107974/1/AliasAbdulRahman2023_HyperspectralImageClassificationUsingMultiLayerPerceptron.pdf; http://eprints.utm.my/107974/; http://dx.doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-179-2023

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.