Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich inf...

Full description

Saved in:
Bibliographic Details
Published in:ISPRS international journal of geo-information 2020-10, Vol.9 (10), p.571
Main Authors: Li, Jinglun, Xiu, Jiapeng, Yang, Zhengqiu, Liu, Chen
Format: Article
Language:English
Subjects:
Artificial neural networks
convolutional neural network
Datasets
Feature extraction
fully convolutional network
Image processing
Image segmentation
Methods
Modular structures
Modules
Neural networks
Remote sensing
remote sensing image
self-attention mechanism
Semantic segmentation
Semantics
Spatial data
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi9100571