High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting

Bibliographic Data

ID22033985
AuthorsYifan Liu (0000-0002-5466-4457, Shandong University of Science and Technology), Qigang Zhu (Shandong University of Science and Technology, corresponding author), Feng Cao (0000-0002-1658-5598, Fujian Anta Logistics Information Technology Co. Ltd., Quanzhou 362200, China), Junke Chen (Shandong University of Finance and Economics), Gang Lu (0000-0003-4753-829X, Shandong University of Science and Technology)
Year2021
Volume10
Issue4
Pages241
Publication date2021-04-07
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10040241
OpenAlexW3143984685
LanguageEN
References cited40

Semantic segmentation has been widely used in the basic task of extracting information from images. Despite this progress, there are still two challenges: (1) it is difficult for a single-size receptive field to acquire sufficiently strong representational features, and (2) the traditional encoder-decoder structure directly integrates the shallow features with the deep features. However, due to the small number of network layers that shallow features pass through, the feature representation ability is weak, and noise information will be introduced to affect the segmentation performance. In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems. AMSM adopts the idea of channel and spatial attention and adaptively fuses three-channel branches by setting branching structures with different void rates, and flexibly generates weights according to the content of the image. AFM uses deep feature maps to filter shallow feature maps and obtains the weight of deep and shallow feature maps to filter noise information in shallow feature maps effectively. Based on these two symmetrical modules, we have carried out extensive experiments. On the ISPRS Vaihingen dataset, the F1-score and Overall Accuracy (OA) reached 86.79% and 88.35%, respectively

Computer vision · Encoder · Segmentation · Weighting · Advanced Image and Video Retrieval Techniques · Advanced Neural Network Applications · Computer Science · Engineering · Remote-Sensing Image Classification · Artificial Intelligence

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