European Journal of Remote Sensing ( IF 4 ) Pub Date : 2020-10-12 , DOI: 10.1080/22797254.2020.1830308 Jinfeng Yan 1 , Menghan Wang 1 , Fenzhen Su 2 , Tian Wang 1 , Ruiming Xiao 1
ABSTRACT
Landsat images have large advantages for studying land cover changes over long time periods and have become the main data source for the extraction of large-scale land cover. However, due to the image resolution limitations, it is difficult to classify second-class land cover types with high accuracy through only traditional automatic classification methods. This study selected the coastal zone of Peninsular Malaysia in Southeast Asia as the experimental area for studying the knowledge rule sets classification method. First, a system for the classification of coastal land cover suitable for Southeast Asia was established based on regional characteristics, which included 8 first-class and 18 second-class types. Then, through the combination of multiple geoscience knowledge rules, a set of knowledge rules for extracting the second-class types was established by integrating multiple sources of information, such as the temporal features, topographic features, texture features, shape features, topological features and spectral features. The experimental results showed that the knowledge rule sets were effective in the extraction of the secondary land types in the coastal zones. The overall classification accuracy was over 85%, and the Kappa coefficient was higher than 0.8. Compared with the traditional supervised classification method, the classification accuracy was obviously improved.
中文翻译:
构建知识规则集以对马来西亚半岛沿海地区的土地覆盖信息进行分类
摘要
Landsat影像对于研究长期的土地覆盖变化具有很大的优势,并且已成为提取大规模土地覆盖的主要数据源。然而,由于图像分辨率的限制,仅通过传统的自动分类方法很难对第二类土地覆被类型进行高精度分类。本研究选择东南亚的马来西亚半岛沿海地区作为研究知识规则集分类方法的实验区域。首先,根据区域特征,建立了适合东南亚地区的沿海土地覆被分类系统,包括8种一类和18种二类。然后,通过多个地球科学知识规则的组合,通过集成多种信息源(例如时间特征,地形特征,纹理特征,形状特征,拓扑特征和光谱特征),建立了用于提取第二类类型的一组知识规则。实验结果表明,知识规则集在提取沿海地区次生土地类型方面是有效的。总体分类精度超过85%,卡伯系数高于0.8。与传统的监督分类方法相比,分类精度明显提高。实验结果表明,知识规则集在提取沿海地区次生土地类型方面是有效的。总体分类精度超过85%,卡伯系数高于0.8。与传统的监督分类方法相比,分类精度明显提高。实验结果表明,知识规则集在提取沿海地区次生土地类型方面是有效的。总体分类精度超过85%,卡伯系数高于0.8。与传统的监督分类方法相比,分类精度明显提高。