基于面向对象与规则的Sentinel-2A影像土地覆被分类——以江西省都昌县为例

土地覆被作为地表自然和人工建造物的综合体，是开展土地科学相关研究的重要基础，在遥感大数据背景下，准确、快速、自动化进行土地覆被提取技术一直是遥感研究中的重点。本文基于eCognition软件，采用面向对象的多尺度分割法，综合考虑地物在遥感影像上的光谱、形状和纹理特征，建立多种地物提取规则。通过模糊函数、支持向量机（SVM）和阈值法对研究区的土地覆被进行分类提取，并与研究区的FROM-GLC10数据和土地利用变更数据进行了对比分析。结果表明：①研究区土地覆被分类的总体精度为97%，Kappa系数为0.96，分类精度较高；②基于10 m分辨率影像，综合使用形状、纹理、光谱信息对于道路的提取具有较好的效果，道路提取Kappa系数为0.84；③分类结果在面积和空间分布上都优于FROM-GLC10数据，与研究区实际土地变更数据保持较好的一致性。基于面向对象与规则的分类方法提取地物能够有效利用多种遥感影像特征，分类精度高，对于处理高分辨率遥感数据具有很好的优势。

Land coverage classification of Sentinel-2A image based on object-oriented and rules: a case study of Duchang county,Jiangxi province

As a comprehensive combination of natural and artificial structures on the surface, land coverage is an important foundation for the development of land science related research. In the background of large remote sensing data, accurate, fast and automatic land coverage extraction technology is focused in remote sensing research all the time. Based on eCognition software, this paper take object-oriented multi-scale segmentation method. Taking account of the spectral, shape and texture characteristics of land objects comprehensively in remote sensing images, established a variety of rules to extract the types of land coverage in the study area by using fuzzy function, support vector machine (SVM) and threshold method. A comparative analysis was also made to compare with FROM-GLC10 data and land use change data in the study area. The results showed that: ① The overall accuracy of land coverage classification in the study area was 97%, Kappa coefficient was 0.96, and the classification accuracy was high. ② Based on 10 m resolution image, the comprehensive use of shape, texture and spectral information had a good effect on the extraction of roads. The Kappa coefficient of road extraction was 0.84. ③ The classification results were better than the FROM-GLC10 data both in area and spatial distribution, as great consistency with the land change data in the study area. The object-oriented and rule-based classification method for feature extraction can effectively utilize a variety of remote sensing image features, and the classification accuracy is high, which has a good advantage for processing high-resolution remote sensing data.