Enhancement of Land Cover and Land Use Classification Accuracy Using Spectral and Textural Features of Fused Images

Kaur, Parminder; Kale, K. V.

doi:10.1007/978-981-13-1423-0_33

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The texture features are an important attribute of remote sensing images, and different land cover types have different texture features. Based on the texture features, the accuracy of the recognition and classification can be improved [46]. The Gray-Level Co-occurrence Matrix (GLCM) is a classic method to extract the texture features [47].…”

Section: Texture Featuresmentioning

confidence: 99%

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

et al. 2021

View full text Add to dashboard Cite

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

show abstract

Section: Texture Featuresmentioning

confidence: 99%