基于Landsat 8卫星影像的北京地区土地覆盖分类

目的 土地覆盖分类能为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术运用于土地覆盖分类具有诸多优势。作为区域性土地覆盖分类应用的重要数据源,Landsat 5/7的TM和ETM+等数据已逐渐失效,Landsat 8陆地成像仪(OLI)较TM和ETM+增加了新的特性,利用Landsat 8数据进行北京地区土地覆盖分类研究,探讨处理方法的适用性。方法 首先,确定研究区域内土地覆盖分类系统,并对Landsat 8多光谱数据进行预处理,包括大气校正、地形校正、影像拼接及裁剪;然后,利用灰度共生矩阵提取全色波段纹理信息,与多光谱数据进行融合;最后,使用支持向量机(SVM)进行分类,获得土地覆盖分类结果。结果 经过精度评价和分析发现,6S模型大气校正和C模型地形校正预处理提高了不同类别之间的可分性,多光谱数据结合全色波段纹理特征能有效提高部分地物的土地覆盖分类精度,总体精度提高2.8%。结论 相对于Landsat TM/ETM+数据,Landsat 8 OLI数据新增特性有利于土地覆盖分类精度的提高。本文方法适用于Landsat 8 OLI数据土地覆盖分类研究与应用,能够满足大区域土地覆盖分类应用需求。

Land cover classification in Beijing using Landsat 8 image

Objective 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. 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. Result 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. Conclusion 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.