地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (8): 1265-1274.doi: 10.12082/dqxxkx.2019.190071

• 遥感科学与应用技术 • 上一篇    下一篇

遥感影像监督分割评价指标比较与分析

李泽宇,明冬萍(),范莹琳,赵林峰,刘思民   

  1. 中国地质大学(北京)信息工程学院,北京 100083
  • 收稿日期:2019-02-21 修回日期:2019-04-05 出版日期:2019-08-28 发布日期:2019-08-28
  • 通讯作者: 明冬萍 E-mail:mingdp@cugb.edu.cn
  • 作者简介:李泽宇(1996-),男,内蒙古包头人,研究方向为遥感信息提取分析。E-mail: henry1830@qq.com
  • 基金资助:
    中国地质大学北京大学生创新创业训练计划项目(201811415118)

Comparison of Evaluation Indexes for Supervised Segmentation of Remote Sensing Imagery

LI Zeyu,MING Dongping(),FAN Yinglin,ZHAO Linfeng,LIU Simin   

  1. School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
  • Received:2019-02-21 Revised:2019-04-05 Online:2019-08-28 Published:2019-08-28
  • Contact: MING Dongping E-mail:mingdp@cugb.edu.cn
  • Supported by:
    Innovation and Entrepreneurship Training Program for College Students of China University of Geosciences, Beijing(201811415118)

摘要:

遥感影像空间分辨率的不断提高,一方面为使用者提供了更加丰富的地物信息,另一方面却也加大了信息准确高效提取的难度。影像分割是遥感影像目标提取的关键步骤,影像分割的效果直接影响信息提取的精度和准度。面对众多分割算法,影像分割效果评价成为遥感信息提取和目标识别研究的重点之一。面向典型目标识别问题,本文针对遥感影像监督分割评价问题,从实验的角度讨论其中具有代表性的面积匹配指数、相似尺寸指标、相关区域指标、质量合格率、欧氏距离指标1、欧氏距离指标2、面积差异指数和距离指标的实际性能与适用情况。首先,通过一系列实验测算不同分割方法下的影像与参考影像的差异情况,讨论测算结果并评估差异指标的优缺点;然后,通过对比分析与加权计算,提出了遥感影像监督分割综合评价方法,实验表明该方法在一定程度上有助于分割方法的科学选择以及影像信息提取效率的提高;最后,从评价指标与分割方法2个角度系统分析了实验结果,并指出了影像监督分割评价存在的问题以及发展趋势。

关键词: 高分辨率遥感影像, 影像分割质量, 监督评价方法, 目标识别, GeOBIA, 差异评价

Abstract:

The improving spatial resolution of remote sensing imagery provides more abundant information for users, but also increases the difficulty of accurate and efficient extraction of information. Image segmentation is a fundamental step in target extraction from remote sensing imagery. The quality of image segmentation directly affects the accuracy of information extraction from high spatial resolution remote sensing imagery. With various segmentation algorithms, image segmentation evaluation has become one of the research focuses in remote sensing information extraction and target recognition. Aiming at the issue of typical target recognition and from an experimental perspective, this paper compared and analyzed in detail eight representative supervised segmentation indexes: Area Fitness Index (AFI), Similarity of Size (SimSize), Relative Area in Sub-Object (RAsub), Quality Rate (QR), Euclidian Distance 1 (ED1), Euclidian Distance 2 (ED2), area discrepancy index (ADI) and Distance-Based Measure (D). Firstly, we employed a series of experiments to calculate the difference between segmentation image and reference image by using different segmentation methods, then discussed the calculation results and evaluated the advantages and disadvantages of the different supervised segmentation evaluation indexes. The comparison results show that the AFI, ED1, ED2 and D could representatively and synthetically assess the segmentation quality. Further, based on the indexes analysis result, this paper proposed a comprehensive evaluation scheme for remote sensing imagery supervised segmentation evaluation by using weighted calculation of the four representative indexes. Through the experiment of comprehensive evaluation, we conclude that the effect of simple shape objects (such as the baseball field and oil tank) by using various segmentation methods is generally ideal. When the shape of objects is complex and the contour is blurred, the accuracy of image segmentation will be sensitive to the segmentation result to some extent. Meanwhile, the effect of segmentation methods (e.g., Otsu-2D, Regional Growth, and Mean Shift) are in general better than the other segmentation methods (e.g., split and merge, maximal entropy, and fuzzy threshold). In addition, the experiments also suggest that the comprehensive method is helpful for the scientific selection of segmentation methods and it can improve the efficiency of information extraction from high spatial resolution imagery. Finally, this paper systematically analyzed the experimental results from the two aspects of evaluation index and segmentation method, and pointed out the existing problems and development trends of image supervised segmentation evaluation.

Key words: high resolution remote sensing image, image segmentation quality, supervisory evaluation method, target recognition, GeOBIA, empirical discrepancy method