Concatenated Residual Attention UNet for Semantic Segmentation of Urban Green Space

Guoqiang Men,Guojin He,Guizhou Wang

doi:10.3390/f12111441

Abstract

Urban green space is generally considered a significant component of the urban ecological environment system, which serves to improve the quality of the urban environment and provides various guarantees for the sustainable development of the city. Remote sensing provides an effective method for real-time mapping and monitoring of urban green space changes in a large area. However, with the continuous improvement of the spatial resolution of remote sensing images, traditional classification methods cannot accurately obtain the spectral and spatial information of urban green spaces. Due to complex urban background and numerous shadows, there are mixed classifications for the extraction of cultivated land, grassland and other ground features, implying that limitations exist in traditional methods. At present, deep learning methods have shown great potential to tackle this challenge. In this research, we proposed a novel model called Concatenated Residual Attention UNet (CRAUNet), which combines the residual structure and channel attention mechanism, and applied it to the data source composed of GaoFen-1 remote sensing images in the Shenzhen City. Firstly, the improved residual structure is used to make it retain more feature information of the original image during the feature extraction process, then the Convolutional Block Channel Attention (CBCA) module is applied to enhance the extraction of deep convolution features by strengthening the effective green space features and suppressing invalid features through the interdependence of modeling channels.-Finally, the high-resolution feature map is restored through upsampling operation by the decoder. The experimental results show that compared with other methods, CRAUNet achieves the best performance. Especially, our method is less susceptible to the noise and preserves more complete segmented edge details. The pixel accuracy (PA) and mean intersection over union (MIoU) of our approach have reached 97.34% and 94.77%, which shows great applicability in regional large-scale mapping.

Highlights

Regarding the concept of urban green space, different regions have their own interpretation of its definition and scope
We propose a new convolutional neural network Concatenated Residual Attention UNet (CRAUNet) for urban green space extraction from GF-1 high-resolution satellite images to better solve the problems in traditional methods
The idea of residual structure and attention mechanism are introduced into the network, and the residual module and Convolutional Block Channel Attention (CBCA) module are proposed to enhance the feature extraction ability while reducing the amount of network parameters and calculations

Summary

Introduction

Regarding the concept of urban green space, different regions have their own interpretation of its definition and scope. Urban green space is an indispensable element in the urban ecosystem which is always considered to be an important component to improve the quality of the urban ecological environment [6]. It provides protection for the sustainable development of the city in various aspects of ecological service functions, such as reducing greenhouse gases, regulation of urban climate, reduction of energy consumption, maintenance of ecological security, etc. Good urban green space monitoring is a necessity for the sustainable development and management of cities [11]. How to accurately and dynamically obtain urban green space information has arisen the interest of researchers

Methods

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Discussion

Conclusion