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Development of object-based change detection method in restricted areas using GIS thematic data

机译:使用GIS专题数据在限制区域的基于对象的变化检测方法的开发

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This paper presents an algorithm-based change detection method for small-scale objects related to nuclear activities using geographic information system (GIS) data. From the nuclear nonproliferation perspective, the structural changes within the significantly suspected area for nuclear activities have to be captured. Additionally, the more amount of satellite imagery increases, e.g., CubeSats, the more systematic approach is required for change detection. Hence, the GIS vector data prescribed for the designated section is introduced as a guide layer in the process of change detection. It is supposed to stay up to date with a final interpretation to reflect the structure status for the next execution. The process of the proposed method consists of four steps: (1) Prior to change detection, satellite imagery of target areas is pre-processed, including the Gram-Schmidt pan-sharpening and image-to-image registration with a second-order rational polynomial coefficient (RPC) and nearest neighbour (NN) interpolation. (2) The before-and-after images obtained from the first step are analysed with the multivariate alteration detection (MAD), which produces pixel-based changes. The MAD output is imported as a change source layer in the process of change detection. (3) multi-temporal image object (MTIO) method is adopted for segmentation with all layers (4-band each and GIS vector layers). (4) The segmented layer stacks up behind the MAD layer to determine whether the MAD layer occupies over the threshold area in each segment, supported by the skeleton-based object linearity index (SOLI) and spectral homogeneity (SH) to minimise the shadow and building-lean effects. The Python programming language customised the MAD analysis, and the ENVI and eCognition support the rest process. The performance of the proposed method is reviewed with pixel-based accuracy assessment (precision, recall, and F1-score), and object-based criterion is also discussed in support of interpretation.
机译:本文介绍了一种基于算法的变化检测方法,用于使用地理信息系统(GIS)数据与核活动相关的小规模对象。从核不扩散的角度来看,必须捕获明显疑似核活动区域内的结构变化。另外,卫星图像的量越多,例如立方体,改变检测需要更系统的方法。因此,在变化检测过程中被引入用于指定部分的GIS向量数据作为引导层。它应该保持最新的解释,以反映下次执行的结构状况。所提出的方法的过程包括四个步骤:(1)在此之前变化检测,目标区域的卫星图像进行预处理,其中包括革兰氏施密特全色锐化和图像到图像配准与第二阶理性多项式系数(RPC)和最近邻(NN)插值。 (2)利用多变量改变检测(MAD)分析从第一步获得的前后图像,其产生基于像素的变化。 MAD输出在变更检测过程中导入为更改源层。 (3)用所有层的分割采用多时间图像对象(MTIO)方法(每个和每个和GIS向量层)。 (4)所述的分段层堆叠起来的MAD层后面,以确定MAD层是否占据超过每个段中的阈值区域中,通过基于骨骼对象线性指数(SOLI)和光谱均匀性(SH)的支持,以最小化阴影和建立瘦效果。 Python编程语言定制了MAD分析,ENVI和Ecognition支持其余过程。通过基于像素的精度评估审查所提出的方法的性能(精确,召回和F1分数),并且还讨论基于对象的标准以支持解释。

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