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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 59; no. 1; pp. 426 - 435
• Main Authors: Ding, Lei, Tang, Hao, Bruzzone, Lorenzo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...