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Object-Based Change Detection of VHR Imagery Based on Extension of Various Pixel-Based Methods

机译：基于多种基于像素方法扩展的基于对象的VHR图像变化检测

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摘要

Change detection (CD), one of the main applications of multitemporal satellite images, is an indicator that directly reflects changes in human activity. Very high resolution (VHR) multitemporal images are useful in CD on the Earth's surface and have abundant information sources, enabling more precise CD analysis. The CD is largely divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). In this study, various PBCD methods were combined with a segmentation result to conduct the OBCD of VHR images. The used PBCD techniques in this study are change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IR-MAD), and principal component analysis (PCA) and K-means clustering from which the binary CD results (i.e., change and no-change) were derived. In order to expand PBCD to OBCD, major voting technique was applied to binary CD results within each object of segmented image that was created using eCognition software. In this process, registration noise (RN) was combined to further improve CD accuracy. The accuracy evaluation of the proposed CD method was conducted using manually digitized reference map. According to the accuracy evaluation, OBCD results that were made by combining all PBCD were the most accurate with 0.393 fl-score. The OBCD results generated by CVA on the side showed the lowest accuracy with 0.301 fl-score.
机译：变化检测（CD）是多时相卫星图像的主要应用之一，是直接反映人类活动变化的指标。高分辨率（VHR）多时相图像在地球表面的CD中非常有用，并且具有丰富的信息源，可以进行更精确的CD分析。 CD大致分为基于像素的更改检测（PBCD）和基于对象的更改检测（OBCD）。在这项研究中，将各种PBCD方法与分割结果相结合以进行VHR图像的OBCD。本研究中使用的PBCD技术是变化向量分析（CVA），迭代加权多变量变更检测（IR-MAD）以及主成分分析（PCA）和K均值聚类，由此得出二进制CD（即变化和无变化）。为了将PBCD扩展为OBCD，主要投票技术应用于使用eCognition软件创建的分割图像的每个对象内的二进制CD结果。在此过程中，套准噪声（RN）被合并以进一步提高CD的准确性。建议的CD方法的准确性评估是使用手动数字化参考图进行的。根据准确性评估，结合所有PBCD得出的OBCD结果以0.393 fl-score最为准确。 CVA在侧面生成的OBCD结果显示最低的准确度是0.301 fl-score。

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