SVM-based classification of multi-temporal Sentinel-2 imagery of dense urban land cover of Delhi-NCR region

Khurana, Yash; Soni, Pramod Kumar; Bhatt, Devershi Pallavi

doi:10.1007/s12145-023-01008-5

Cited by 2 publications

(2 citation statements)

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“…Train datasets are used to select the best hyperplane, while test datasets are used to validate its generalization capabilities [59]. SVM has been successfully used in many LC classification studies [60][61][62]. Kernel function (Type) is the crucial parameter in the SVM algorithm.…”

Section: Image Classificationmentioning

confidence: 99%

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Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

Moharrami,

Attarchi,

Gloaguen

et al. 2024

Remote Sensing

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Reliable and up-to-date training reference samples are imperative for land cover (LC) classification. However, such training datasets are not always available in practice. The sample migration method has shown remarkable success in addressing this challenge in recent years. This work investigated the application of Sentinel-1 (S1) and Sentinel-2 (S2) data in training sample migration. In addition, the impact of various spectral bands and polarizations on the accuracy of the migrated training samples was also assessed. Subsequently, combined S1 and S2 images were classified using the Support Vector Machines (SVM) and Random Forest (RF) classifiers to produce annual LC maps from 2017 to 2021. The results showed a higher accuracy (98.25%) in training sample migrations using both images in comparison to using S1 (87.68%) and S2 (96.82%) data independently. Among the LC classes, the highest accuracy in migrated training samples was found for water, built-up, bare land, grassland, cropland, and wetland. Inquiries on the efficiency of different spectral bands and polarization used in training sample migration showed that bands 4 and 8 and VV polarization in the water class were more important, while for the wetland class, bands 5, 6, 7, 8, and 8A together with VV polarization showed superior performance. The results showed that the RF classifier provided better performance than the SVM (higher overall, producer, and user accuracy). Overall, our findings suggested that shared use of S1 and S2 data can be used as a suitable means for producing up-to-date and high-quality training samples.

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Section: Image Classificationmentioning

confidence: 99%

“…SVM kernel functions are generally classified into four clusters as follows: linear, polynomial, Radial Basis Functions (RBF), and sigmoid kernels [59]. However, RBF is mainly applied in the literature for LC classification and it has provided good performance [60,63]. Based on this, the RBF function was used in the SVM algorithm.…”

Section: Image Classificationmentioning

confidence: 99%