Extraction of maize planting area based on transformer and remote sensing data

Wei Lv; Xuan Song; Huan Yang

doi:10.1117/12.2689430

10 August 2023 Extraction of maize planting area based on transformer and remote sensing data

Wei Lv, Xuan Song, Huan Yang

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127481L (2023) https://doi.org/10.1117/12.2689430
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

As one of the main food crops in China, timely and accurate monitoring of the planting area and acreage of corn is of great significance for the evaluation of agricultural productivity and ensuring food security.Based on ESA Sentinel-2 MSI remote sensing image data, the NDVI time series curves are extracted with the support of Google Earth Engine cloud platform, the transformer model is built, and the time series data are input into the model to obtain the typical feature classification results, and the maize planting areas in typical agricultural areas of North China Plain are extracted, and the accuracy is verified by using field survey data, and compared with convolutional neural network The results were compared with those of random forest classification and convolutional neural network. The results show that the overall accuracy of transformer classification is higher and the classification effect is better compared with random forest and convolutional neural network algorithms. Therefore, the use of transformer can effectively improve the crop planting area extraction accuracy.

Citation Download Citation

Wei Lv, Xuan Song, and Huan Yang "Extraction of maize planting area based on transformer and remote sensing data", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127481L (10 August 2023); https://doi.org/10.1117/12.2689430

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KEYWORDS

Transformers

Remote sensing

Feature extraction

Random forests

Vegetation

Image classification

Convolutional neural networks

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