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Responding to the shortcomings of China's civil remote sensing data in land cover classification, such as the difficulty of data acquisition and the low utilization rate, we used Landsat-8, China Orbita Zhuhai-1 hyperspectral remote sensing (OHS) data, and Landsat-8 + OHS data combined with band (red, green, and blue) and vegetation index features to classify land cover using maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM). The results show that Landsat-8 + OHS data have the highest classification accuracy in SVM, with an overall accuracy of 83.52% and a kappa coefficient of 0.71, and this result is higher than that of Landsat-8 images and OHS images separately. In addition, the classification accuracy of OHS images was higher than that of Landsat-8 images. The results of the study provide a reference for the use of civil satellite remote sensing data in China.
Li et al. (Sun,) studied this question.