Tehnički vjesnik, Vol. 29 No. 5, 2022.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20220421142959
MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images
Fuping Zeng
; School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Bin Yang
orcid.org/0000-0002-9207-4511
; Du Xiaoman (Beijing) Science Technology Co., Ltd., Beijing 100094, China
Mengci Zhao
orcid.org/0000-0002-8880-8869
; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Ying Xing
orcid.org/0000-0003-2807-1911
; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yiran Ma
; School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Sažetak
As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria.
Ključne riječi
global context information; MASANet; multi-scale features; semantic segmentation
Hrčak ID:
281670
URI
Datum izdavanja:
15.10.2022.
Posjeta: 619 *