LABANet: A Lightweight Asymmetrical Bottleneck and Attention-Based Network for Cloud Detection

Recently, onboard satellite cloud detection has attracted extensive interest due to its ability to reduce invalid data and save satellite storage and downlink resources. As the demand for accuracy has increased, deep neural networks with encoder-decoder architectures have been widely used for cloud...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE sensors journal Jg. 24; H. 4; S. 4771 - 4785
Hauptverfasser:	Yu, Ximing, Peng, Yu, Shao, Wenyi, Liu, Liansheng, Sun, Kaipeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Artificial neural networks Asymmetrical bottleneck Asymmetry Cloud computing Coders Computer architecture Correlation Data mining Downlink Feature extraction Floating point arithmetic Lightweight lightweight deep neural network onboard satellite cloud detection Parameters Program processors Satellites Sensors spatial and channel attention
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!