HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Copernicus Gesellschaft Mbh
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
ISPRS WG IV/7 Geoinformation Week on Broadening Geospatial Science and Technology -- NOV 14-17, 2022 -- ELECTR NETWORK
Anahtar Kelimeler
LULC Mapping, Big data, Hyperspectral, Image Classification, Machine Learning, Multi-layer Perceptron
Kaynak
Geoinformation Week 2022, Vol. 48-4
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
