MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this letter, we incorporate multiscale fe...

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Vydáno v:	IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autoři:	Li, Rui, Duan, Chenxi, Zheng, Shunyi, Zhang, Ce, Atkinson, Peter M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Asymmetric convolution block (ACB) Asymmetry Coders Convolution Decoding Design Economics Feature extraction Feature maps fine-resolution remotely sensed images Image processing Image segmentation Kernel Land resources Remote sensing Remote sensors Resolution Resource management Semantic segmentation Semantics Spacecraft recovery
ISSN:	1545-598X, 1558-0571
On-line přístup:	Získat plný text
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