Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model

• Published in: International journal of remote sensing 2020-02, Vol.41 (3), p.953-973
• Main Authors: Wang, Xiaoping, Zhang, Fei, Kung, Hsiang-te, Johnson, Verner Carl, Latif, Aamir
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Detection; Image classification; Image filters; Image processing; Image resolution; Imagery; Integers; Model accuracy; Order processing; Procedures; Remote sensing; Salinization; Search algorithms; Soil improvement; Soil salinity; Soil salinization; Soils; Support vector machines
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2019.1654142

The remote sensing information on the extraction method is of great importance to improve the accuracy and efficiency of soil salinization information. The objective of this study is to develop remote sensing extraction techniques to improve soil salinization maps. The following procedures were used in this study: (1) developed a fractional-order algorithm-based methodology of filter from high-resolution remote sensing imagery (Sentinel-2 MSI); (2) investigated the changing trend of image under different order filters; and (3) used a grid-search algorithm-support vector machines (GS-SVM) classification to employ extraction information of soil salinization. The results showed that the Fractional-order filter method outperformed the integer derivative in extracted information of soil salinization. In comparison of the classification accuracy between fractional-order processing algorithm and integer-order image processing algorithm, the fractional order has improved remarkably. The optimal classification model was 0.6 order, 0.8 order, 1.4 order, 1.6 order, and 1.8 order models. The overall accuracy and kappa coefficient (κ) of these models are 91.90% and 0.90, respectively. Analysing and comparing between soil salt index and filtering algorithm (1.2 order), the researchers found that the classification results of the two methods are similar. In general, this method can successfully extract soil salinization information in dry regions.