Crop classification using non-fixed length multitemporal images base on deep learning

Wei Leng; Wenqiang Li; Xiaolin Han; Huan Zhang; Weidong Sun

doi:10.1117/12.3059111

2 January 2025 Crop classification using non-fixed length multitemporal images base on deep learning

Wei Leng, Wenqiang Li, Xiaolin Han, Huan Zhang, Weidong Sun

Author Affiliations +

Proceedings Volume 13514, International Conference on Remote Sensing and Digital Earth (RSDE 2024); 135140W (2025) https://doi.org/10.1117/12.3059111
Event: 2024 International Conference on Remote Sensing and Digital Earth, 2024, Chengdu, China

Abstract

Previous studies on crop classification methods based on deep learning for multitemporal images had already determined the number of inputs multi temporal images in the network structure design stage. However, in reality, due to satellite revisit cycles, weather, and other reasons, stable and clear remote sensing images (RSIs) cannot be continuously obtained. Once a period of image is missing from the multi temporal image sequence, the entire method cannot be used. Although methods such as interpolation and using other images instead can be used to address this issue, they greatly reduce the classification accuracy and stability of the methods, limiting their large-scale application. In response to the above issues, we first proposed a flexible multi temporal RSI dataset. For this dataset, an improved version UNet is constructed to train the model. Crop classification experiments shows that this model can be used without limiting the number of RSI periods and time inputs, and the classification accuracy gradually increases with the increase of image periods.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Wei Leng, Wenqiang Li, Xiaolin Han, Huan Zhang, and Weidong Sun "Crop classification using non-fixed length multitemporal images base on deep learning", Proc. SPIE 13514, International Conference on Remote Sensing and Digital Earth (RSDE 2024), 135140W (2 January 2025); https://doi.org/10.1117/12.3059111

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KEYWORDS

Image classification

Clouds

Education and training

Data modeling

Deep learning

Image processing

Machine learning

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