Land cover classification can provide important information for ecosystems

water resource

and climate models. Remote sensing technology has many advantages in land cover classification because of its continuous coverage at the spatial scale and continuous observation at the time scale. The Landsat-5 TM and the Landsat-7 ETM+ sensors

which are important remote sensing data sources for regional land cover classification applications

have failed successively. Landsat-8 continues the mission of earth observation of the Landsat series. The OLI sensor of Landsat-8 has several new characteristics

which include adding a deep blue band and cirrus band

narrowing the spectral range of the near-infrared band

and increasing the radiation resolution and the signal-to-noise ratio. This study investigates the method of land cover classification in Beijing using Landsat-8 OLI data and discusses the feasibility of the method. First

the land cover classification system that is suitable for the study area and the spatial resolution of OLI sensor are determined

and the data of Landsat-8 multispectral images that cover the wholearea of Beijing are subjected to preprocessing

including atmospheric correction (using 6S model)

topographic correction (using C model)

image mosaicking

and extraction. Then

the texture images (at four different scales) of panchromatic band are extracted using gray-level co-occurrence matrix

and the texture images are resampled to obtain texture features.To improve classification accuracy

the texture features are fused with the multispectral data

and land cover is classifiedby using a support vector machine. Finally

precision evaluation is performed by using a confusion matrix

and the overall accuracy and Kappa coefficient of the method is determined by using classified images that use spectral features only and classified images that use spectral features and texture features. The results of the study are as follows: (1)With regard to the preprocessing methods of Landsat-8 OLI data

atmospheric correction using 6S model and topographic correction using C model can improve class separability between different land cover types in varying degrees. (2) In terms of the use of texture features in land cover classification of Landsat-8 data

the addition of texture information of panchromatic band in Landsat-8 can effectively improve the accuracy of classification of some land covers(such as forest

crop

building

and bare land);the overall classification accuracy is improved by 2.8%

and the kappa coefficient is improved by 0.0336. (3) In terms of extracting the texture features of Landsat-8 panchromatic band

5×5 window is the most suitable scale

compared with 3×3

7×7

and 9×9 windows

in land cover classification of Landsat-8 data. Compared with Landsat TM/ETM+ data

the new characteristics of the Landsat OLI data help promote the use of Landsat-8 data in remote sensing land cover classification. The proposed method is suitable for research and application of land cover classification using Landsat-8 OLI data and can satisfy the requirements for land cover classification in large regions.