Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images

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@Article{wu:2021:RS,

• author = "Zhenjiang Wu and Jiahua Zhang and Fan Deng and Sha Zhang and Da Zhang and Lan Xun and Mengfei Ji and Qian Feng",
• title = "Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 {SAR} and Sentinel-2 Multispectral Images",
• journal = "Remote Sensing",
• year = "2021",
• volume = "13",
• number = "20",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2072-4292",
• URL = "https://www.mdpi.com/2072-4292/13/20/4067",
• DOI = "doi:10.3390/rs13204067",
• abstract = "Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and use of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimised classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimised classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21percent and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.",
• notes = "also known as \cite{rs13204067}",

}

Genetic Programming entries for Zhenjiang Wu Jiahua Zhang Fan Deng Sha Zhang Da Zhang Lan Xun Mengfei Ji Qian Feng

Citations