Buildings extraction of GF-2 remote sensing image based on multi-layer perception network

doi:10.6046/gtzyyg.2020289

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Abstract

The task of extracting buildings with high-resolution remote sensing image plays an important role in urban planning and urbanization. In view of the problems of existing deep learning extraction methods, for example, the shallow features can’t been used effectively and small target information is easily lost, this paper proposes a multi-level perceptual network. This network uses dense connection mechanism to fully extract feature information, and constructs parallel structure to retain spatial information of different feature resolution and enhance feature information of different depth and scale in order to reduce the loss of detail feature. At the same time, the ASPP module is used to obtain the information of different receptive fields and extract the deep architectural features at different scales. The experimental results show that the overall accuracy of the proposed method is 97.19%, intersection over union is 74.33% and theF1 score is 85.43% in the buildings extraction of GF-2 remote sensing image, all of which are higher than those of the traditional method and other deep learning methods. In addition, buildings with multi-source remote sensing images still have good extraction effect, which reflects the practicability of the method presented in this paper.

Keywords deep learning remote sensing images building extraction multiscale feature fusion

ZTFLH:

TP79

Corresponding Authors: WU Yanlan E-mail: luqi9507@163.com;wuyanlan@ahu.edu.cn

Issue Date: 21 July 2021

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	Qi LU
	Jun QIN
	Xuedong YAO
	Yanlan WU
	Haochen ZHU

Cite this article:

Qi LU,Jun QIN,Xuedong YAO, et al. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020289 OR https://www.gtzyyg.com/EN/Y2021/V33/I2/75

Fig.1 Flow chart of network structure

Fig.2 A denseblock with three convolutional layers

Fig.3 Atrous spatial pyramid pooling

Fig.4 Three feature fusion methods

Fig.5 Division scope of training area and testing area in Hefei

Tab.1 Image parameters

Tab.2 image and label data of building

Fig.6 Result display

Tab.3 Test accuracy(%)

Tab.4 Comparison of extraction accuracy with traditional methods(%)

Tab.5 Comparison of extraction results with traditional methods

Tab.6 Comparison of extraction accuracy with classic deep learning network model(%)

Tab.7 Comparison of extraction results with classic deep learning network model

Fig.7-1 Multisource remote sensing image test results

Fig.7-2 Multisource remote sensing image test results

Tab.8 Test accuracy(%)

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