Analysis of winter wheat recognition ability based on multiphase Sentinel-2A data

Fanchen Peng; Shuhe Zhao; Wenting Cai; Yamei Wang; Zhaohua Zhang

doi:10.1117/12.2324724

23 October 2018 Analysis of winter wheat recognition ability based on multiphase Sentinel-2A data

Fanchen Peng, Shuhe Zhao, Wenting Cai, Yamei Wang, Zhaohua Zhang

Proceedings Volume 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII; 1078012 (2018) https://doi.org/10.1117/12.2324724
Event: SPIE Asia-Pacific Remote Sensing, 2018, Honolulu, Hawaii, United States

Abstract

Effective and dynamic recognition of winter wheat has important implications for the development of agriculture in In this paper, we proposed a method for winter wheat identification using particle swarm optimization-support vector machine (PSO-SVM) model and multi-temporal Sentinel-2A image. The eigenvector combination based on spectral information and the eigenvector combination based on texture information were constructed by using different phenological periods of winter wheat. The winter wheat was identified and extracted by PSO-SVM. The extraction accuracy under different feature band combinations was compared and analyzed. The results showed that PSO-SVM had higher accuracy than traditional SVM. Using PSO-SVM, the optimal combination was multi-temporal spectral and mean texture information combination and its classification accuracy was 91.25%. This paper provides a theoretical basis for the future use of Sentinel-2A data to extract other crop information.

Citation Download Citation

Fanchen Peng, Shuhe Zhao, Wenting Cai, Yamei Wang, and Zhaohua Zhang "Analysis of winter wheat recognition ability based on multiphase Sentinel-2A data", Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 1078012 (23 October 2018); https://doi.org/10.1117/12.2324724

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KEYWORDS

Image classification

Feature extraction

Remote sensing

Associative arrays

Particles

Particle swarm optimization

Principal component analysis

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