LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images

The trade-off between feature representation power and spatial localization accuracy is crucial for the dense classification/semantic segmentation of remote sensing images (RSIs). High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurre...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2021-01, Vol.59 (1), p.426-435
Hauptverfasser:	Ding, Lei, Tang, Hao, Bruzzone, Lorenzo
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Convolutional neural network (CNN) Convolutional neural networks Correlation Decoding deep learning Embedding Feature extraction Geographical distribution Image classification Image processing Image segmentation Localization Modules Neural networks Remote sensing Representations Semantic segmentation Semantics Spatial discrimination Spatial distribution
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