Semantic segmentation of remote sensing images based on attention mechanism and feature fusion

Jinnan Pan; Shizhen Huang

doi:10.1117/12.2681079

23 May 2023 Semantic segmentation of remote sensing images based on attention mechanism and feature fusion

Jinnan Pan, Shizhen Huang

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451Q (2023) https://doi.org/10.1117/12.2681079
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In this article, we design this algorithm to perform the segmentation task of remote sensing images by using attention module and feature fusion to deal with the challenges of features with different dimensions, small-size objects and uncertain boundary segmentation in remote sensing images. The encoder uses a PVTv2 model to increase the feature information acquisition capability of the model, and several convolutional paths of the feature aggregation module can acquire different size information of the image. The deep network uses attention mechanisms to obtain features that are valid for the segmentation task. In the decoding phase, the feature fusion module fuses the shallow network detail information with the deep network semantic information. The experimental comparison on the ISPRS Potsdam dataset shows that pixel accuracy of the model in this paper achieves 88.59%, IoU, mIoU, and FWIoU are higher than some classical semantic segmentation networks, which effectively increases the accuracy of remote sensing image segmentation.

Citation Download Citation

Jinnan Pan and Shizhen Huang "Semantic segmentation of remote sensing images based on attention mechanism and feature fusion", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451Q (23 May 2023); https://doi.org/10.1117/12.2681079

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KEYWORDS

Image segmentation

Remote sensing

Feature fusion

Image fusion

Semantics

Data modeling

Transformers

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