Abstract:

The GF-1 satellite has advantages such as short revisit period and the combination of various spatial resolutions and large swath, while the Landsat-8 satellite has the advantages of multi-channels and high radiometric resolution. This article presents a case study focused on land cover classification in Zhongxiang City, Hubei Province. Considering the characteristics of different sensor parameters, the Support Vector Machine classifier (SVM) was applied to the two datasets of the same region on 6 August 2013 and a comparison was made on the classification results. The result shows that the coefficient of determination of the corresponding channels between these two sensors are over 0.92. Good consistencies are found in typical samples’ spectrum. However, compared to GF-1, Landsat-8 has better separability between farmland and woodland, and between impervious surface and bare soil. The overall classification accuracy of GF-1 and Landsat-8 reaches 90.38% and 90.07% respectively, whereas there are differences in classification accuracies of different surface types. The differences in spectral response functions may account for the advantage of Landsat-8 on woodland identification accuracy as compared to GF-1. In addition, compared to Landsat-8, GF-1 outperforms in classification accuracy on surface types with fragmented distribution because GF-1 has higher spatial resolution.

Key words: comparison of land cover classifications, spectrum of surface types, separability of surface types, GF-1 image, Landsat-8 image