Integrating spectral and textural attributes to measure magnitude in object-based change vector analysis
Integrating spectral and textural attributes to measure magnitude in object-based change vector analysis
复制标题
集成光谱和纹理属性来测量基于对象的变化矢量分析的幅度
DOI：
10.1080/01431161.2019.1582111
复制
发表时间：
2019-02
影响因子：
3.4
通讯作者：
Qiang Chen
中科院分区：
文献类型：
--
作者：
Hao Sun;Wei Zhou;Yixiu Zhang;Chuangchuang Cai;Qiang Chen
随着高或极高空间分辨率遥感图像可及性的不断提高，越来越多的变化检测工作以图像对象为单位进行，即基于对象的变化检测（OBCD）。变更向量分析（CVA）是一种很有前途的无监督OBCD工具，因为它可以提供对变更的合理解释和对变更类型的洞察。然而，图像对象的各种特征和属性产生了复杂的高维特征空间，这给CVA整体变化幅度和方向的测量带来了新的挑战。本文提出了一种自适应地综合图像目标的光谱属性和纹理属性来测量图像目标整体变化幅度的新方法。将新方法与标准CVA大小、马氏距离和最近采用相同OTSU自动阈值方法的无监督OBCD自适应加权CVA （SAW-CVA）大小进行了比较。案例1使用双时相WorldView多光谱图像（4波段），案例2使用从谷歌地球获取的双时相三波段图像。两种情况下的结果都表明，新方法优于其他无监督OBCD方法。病例1新入路、SAW-CVA、标准CVA、Mahalanobis分类正确率分别为80.28%、77.93%、72.77%、61.50%，病例2为91.80%、90.16%、88.52%、90.16%。进一步分析表明，与现有的SAW-CVA方法相比，新方法给出的自适应权重更合理，且不需要经验自适应指标。
ABSTRACT With the increasing accessibility of high or very high spatial resolution remote sensing images, more and more change detection works are conducted on the unit of image object, i.e. the object-based change detection (OBCD). Change vector analysis (CVA) is a promising tool for unsupervised OBCD because it can provide reasonable interpretation of the change and an insight into the type of change. However, various features and attributes of image object produce complex high dimensional feature space that poses new challenges to measure the overall change magnitude and direction in CVA. This paper presents a new approach to measure the overall change magnitude of an image object by integrating its spectral and textural attributes, self-adaptively. The new approach was compared with the standard CVA magnitude, Mahalanobis distance, and a recent Self-Adaptive Weight CVA (SAW-CVA) magnitude for unsupervised OBCD with the same OTSU auto-thresholding method. Two cases were investigated where Case I used bi-temporal WorldView multispectral images (4-band) and Case II employed bi-temporal three-band images obtained from Google Earth. Results in the two cases both demonstrated that the new approach outperforms the others for unsupervised OBCD. The percentage correct classification of the new approach, SAW-CVA, standard CVA, and Mahalanobis are 80.28%, 77.93%, 72.77%, and 61.50% in Case I, and 91.80%, 90.16%, 88.52%, and 90.16% in Case II. Further analysis indicated that the new approach gives more reasonable self-adaptive weights and it does not require the empirical self-adaptive index as compared with the recent SAW-CVA method.
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DOI：
10.1016/j.jag.2014.12.015
发表时间：
2015-06
期刊：
Int. J. Appl. Earth Obs. Geoinformation
影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
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作者：
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DOI：
10.3233/fi-2000-411207
发表时间：
2000
期刊：
Fundam. Informaticae
影响因子：
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作者：
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J. Roerdink;Arnold Meijster
影响因子：
13.5
作者：
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影响因子：
13.5
作者：
Lu, Miao;Chen, Jun;Wu, Wenbin
通讯作者：
Wu, Wenbin