WETLAND CLASSIFICATION WITH SWIN TRANSFORMER USING SENTINEL-1 AND SENTINEL-2 DATA
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Convolutional Neural Networks (CNNs) have shown promising results in classifying complex remote sensing scenery, particularly in the classification of wetlands. State-of-the-art Natural Language Processing ( NLP) algorithms, on the other hand, are transformers. In this paper, we illustrate the effectiveness of the cutting-edge Swin Transformer for the classification of complex wetlands in New Brunswick, Canada. The precision of the proposed transformer is 0.66, 0.71, 0.75, 0.78, 0.82, 0.83, 0.84, 0.90, 0.90, 0.95, and 0.98 for the recognition of shrub, fen, forested wetland, crop, bog, freshwater marsh, coastal marsh, aquatic bed, grass, urban, and water, respectively. Based on the results, with a relatively high level of overall accuracy of slightly less than 80%, the proposed Swin Transformer is highly capable of complex wetland classification.
Açıklama
IEEE International Geoscience and Remote Sensing Symposium (IGARSS) -- JUL 17-22, 2022 -- Kuala Lumpur, MALAYSIA
Anahtar Kelimeler
Wetland mapping, CNN, Transformer, Swin Transformer, Sentinel imagery
Kaynak
2022 Ieee International Geoscience and Remote Sensing Symposium (Igarss 2022)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
