Automatic Acquisition of Appropriate Codewords Number in BoVW Model and the Corresponding Scene Classification Performance

Li, Fang

doi:10.23919/chicc.2018.8484002

2018 37th Chinese Control Conference (CCC) 2018

DOI: 10.23919/chicc.2018.8484002

|View full text |Cite

Automatic Acquisition of Appropriate Codewords Number in BoVW Model and the Corresponding Scene Classification Performance

Fang Li

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery

Zeng¹,

Lin

Sun

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Remote sensing scene classification aims to automatically assign proper labels to remote sensing images. Most of the existing deep learning based methods usually consider the interclass and intraclass relationships of the image content for classification. However, these methods rarely consider the hierarchical information of scene labels, as a scene label may belong to hierarchically multi-grained levels. For example, multi-grained level labels may indicate that a remote sensing scene image may belong to the coarse-grained label “transportation land” while also belonging to the fine-grained label “airport”. In this paper, to exploit hierarchical label information, we propose an attention-embedding multi-task multi-grained network (AEMMN) for remote sensing scene classification. In the proposed AEMMN, we add a coarse-grained classifier as the first level and a fine-grained classifier as the second level to perform multi-task learning tasks. Additionally, a gradient control module is utilized to control the gradient propagation of two classifiers to suppress the negative transfer caused by the irrelevant features between tasks. In the feature extraction portion, the model uses an ECA module embedding Resnet50 to extract effective features with cross-channel interaction information. Furthermore, an external attention module is exploited to improve the discrimination of fine-grained and coarse-grained features. Experiments were conducted on the NWPU-RESISC45 and the Aerial Image Data Set (AID), and the overall accuracy of the proposed AEMMN is 92.07% on the NWPU-RESISC45 dataset and reached 94.96% on the AID. The results indicate that hierarchical label information can effectively improve the performance of scene classification tasks when categorizing remote sensing imagery.

show abstract

Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery

Zeng¹,

Lin

Sun

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Automatic Acquisition of Appropriate Codewords Number in BoVW Model and the Corresponding Scene Classification Performance

Cited by 1 publication

References 32 publications

Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery

Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery

Contact Info

Product

Resources

About