Assessing the quality of training data in the supervised classification of remotely sensed imagery: a correlation analysis

作者：Ge, Yong^*; Bai, Hexiang; Wang, Jinfeng; Cao, Feng

来源：Journal of Spatial Science, 2012, 57(2): 135-152.

DOI：10.1080/14498596.2012.733616

• quality assessment of training data
• rough set
• measures
• correlation analysis

Training data play an important role in the supervised classification process of remotely sensed images. Its quality is an important factor affecting the accuracy of image classification. Therefore, measuring the quality of training data is essential for classification procedures and subsequent operations. This paper discusses a new method for the quality assessment of training data before the classification procedure and investigates the correlation between measures for training data and measures for classified images at category and image level, respectively. Five groups of sample data collected from a Landsat TM image were used in correlation analyses. The results demonstrate that the proposed method is valid for measuring the quality of training data and can, to some extent, reflect the quality of classified images which are obtained through supervised classification with the corresponding training dataset.

• 出版日期2012
• 单位中国科学院; 资源与环境信息系统国家重点实验室; 中国科学院地理科学与资源研究所; 山西大学