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Article

DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

by 1,2, 1,2,*, 1,2 and 1,2
1
Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(3), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030125
Received: 7 December 2020 / Revised: 11 February 2021 / Accepted: 23 February 2021 / Published: 1 March 2021
(This article belongs to the Special Issue Geospatial Artificial Intelligence)
Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%. View Full-Text
Keywords: land cover; semantic segmentation; convolution neural network land cover; semantic segmentation; convolution neural network
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MDPI and ACS Style

Huang, J.; Weng, L.; Chen, B.; Xia, M. DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover. ISPRS Int. J. Geo-Inf. 2021, 10, 125. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030125

AMA Style

Huang J, Weng L, Chen B, Xia M. DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover. ISPRS International Journal of Geo-Information. 2021; 10(3):125. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030125

Chicago/Turabian Style

Huang, Junqing, Liguo Weng, Bingyu Chen, and Min Xia. 2021. "DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover" ISPRS International Journal of Geo-Information 10, no. 3: 125. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030125

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