Jamali, AliMahdianpari, MasoudRahman, Alias Abdul2024-09-292024-09-2920231682-17502194-9034https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-179-2023https://hdl.handle.net/20.500.14619/7752ISPRS WG IV/7 Geoinformation Week on Broadening Geospatial Science and Technology -- NOV 14-17, 2022 -- ELECTR NETWORKThe 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.eninfo:eu-repo/semantics/openAccessLULC MappingBig dataHyperspectralImage ClassificationMachine LearningMulti-layer PerceptronConference Object10.5194/isprs-archives-XLVIII-4-W6-2022-179-20232-s2.0-85148765461182N/A179WOS:001185691400024N/A