An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
This work addresses the problem of improving classification accuracy for remote sensing image analysis, which is incremental as it builds on existing deep learning methods with specific architectural enhancements.
The paper tackled the challenge of feature fusion in semantic segmentation of very-high-resolution remote sensing imagery by proposing an attention-fused network (AFNet), achieving state-of-the-art performance with overall accuracies of 91.7% and 92.1% on two datasets.
Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset.