Integrating Data and Model Optimization for Improved Multi-Class Land Cover Classification Using Multispectral Remote Sensing Data

Abstract. With the rapid development of artificial intelligence, significant progress has been made in land cover classification using deep learning methods. However, in existing research, most studies focus more on improving classification accuracy by optimizing the model structure and less on mining the value of the data itself. In this paper, experiments on remote sensing multi-class land cover classification were conducted based on Worldview3 data, and strategies to improve classification accuracy were proposed in terms of sampling methods, band combination, loss function, and model optimization. Experiment results show that the proposed improvement strategies are effective for multi-class land cover classification, with recall, F1, and IoU improved by 29%, 17%, and 19%, respectively. The significant improvement in classification accuracy for less-represented targets confirms that enhancing data richness and balance leads to greater improvement than just optimizing the model.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Integrating Data and Model Optimization for Improved Multi-Class Land Cover Classification Using Multispectral Remote Sensing Data ; volume:XLVIII-1-2024 ; year:2024 ; pages:359-364 ; extent:6 ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-1-2024 (2024), 359-364 (gesamt 6)

Contributors, Places and Time

• Creator: Li, Yichuan ; Sun, Huanying ; Yu, Junchuan ; Xie, Minying ; Jin, Dingjian ; Wang, Ming ; Xia, Guobin

Further information

• DOI: 10.5194/isprs-archives-XLVIII-1-2024-359-2024
• URN: urn:nbn:de:101:1-2405160451008.366424379596
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:54 AM CEST