An improved architecture for urban building extraction based on depthwise separable convolution

Accurate extraction of urban buildings is a key problem in urban remote sensing image processing. It can be applied to many kinds of urban problems, such as data statistics of urban management and smart cities. In recent years, the deep learning model based on convolutional neural network is widely...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2020-01, Vol.38 (5), p.5821-5829
Main Authors: Zhang, Xiaoqing, Zheng, Yongguo, Liu, Weike, Peng, Yanjun, Wang, Zhiyong
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer architecture
Convolution
Detection
Image processing
Image segmentation
Machine learning
Neural networks
Optimization
Remote sensing
Target recognition
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:Accurate extraction of urban buildings is a key problem in urban remote sensing image processing. It can be applied to many kinds of urban problems, such as data statistics of urban management and smart cities. In recent years, the deep learning model based on convolutional neural network is widely used in the field of target recognition and semantic segmentation. In this paper, based on U-Net for urban building extraction from remote sensing image, we propose a neural network architecture for urban building extraction from remote sensing image. We use depth separable convolution to improve it and adjust the process of network super parametric optimization according to the characteristics of building. We call this new architecture XU-Net. We evaluate the performance of XU-Net through experiments with INRIA aerial image data set. The result shows that XU-Net is not only feasible but also efficient. Moreover, XU-Net reduces number of parameters 89%, from 18.8M to 2.13M, compared to classical architecture U-Net, at the same time, it guarantees the accuracy can reach 97.5%.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-179669