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首页> 外文期刊>International journal of remote sensing >A high-level data fusion and spatial modelling system for change-detection analysis using high-resolution airborne digital sensor data
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A high-level data fusion and spatial modelling system for change-detection analysis using high-resolution airborne digital sensor data

机译：使用高分辨率机载数字传感器数据进行变化检测分析的高级数据融合和空间建模系统

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A high-level data fusion system that uses Bayesian statistics involving weights-of-evidence modelling is described to combine disparate information from airborne digital data such as digital surface model (DSM), colour, thermal infrared (TIR) and hyperspectral images at different time periods. To determine the efficacy of the system, an analysis of change detection was performed. The data fusion system is capable of detecting changes in man-made features automatically in a densely populated area where there is little prior information. Multiclass segmented images were obtained from the data captured by four airborne remote sensing sensors. The system performs data fusion modelling by using binary images of each theme class and a total of 40 binary patterns were obtained. Through Bayesian methods, involving weights-of-evidence modelling, all the binary images were analysed and finally four binary patterns (indicator images) were identified automatically as significant for the change-detection application. A weighted index overlay model available in the system combines these four patterns. Data fusion by weights-of-evidence modelling is found to be straightforward and unequivocal for predicting newly transformed locations. The results of the Bayesian method are accurate as the weights are based on statistical analysis. Changes in features such as colour of roofs, parking areas, openland areas, newly built structures, and the presence or absence of vehicles are extracted automatically by using the high-level data fusion approach. The final predictor image shows the probability of change-detected areas in a densely populated city in Japan.
机译：描述了一种使用贝叶斯统计（涉及证据权重建模）的高级数据融合系统，以结合来自机载数字数据的不同信息，例如在不同时间的数字表面模型（DSM），彩色，热红外（TIR）和高光谱图像期。为了确定系统的效率，对变更检测进行了分析。数据融合系统能够在人口稠密的地区（几乎没有先验信息）自动检测人造特征的变化。从四个机载遥感传感器捕获的数据中获得了多类分割图像。该系统通过使用每个主题类的二进制图像执行数据融合建模，并获得了总共40个二进制模式。通过涉及证据权重建模的贝叶斯方法，分析了所有二进制图像，最后自动识别出四个二进制模式（指标图像）对变更检测应用很重要。系统中可用的加权索引覆盖模型将这四个模式结合在一起。通过证据权重建模进行的数据融合对于预测新近转换的位置而言是直接且明确的。贝叶斯方法的结果是准确的，因为权重基于统计分析。通过使用高级数据融合方法，可以自动提取屋顶，停车场，空地，新建建筑物以及是否有车辆等特征的变化。最终预测图像显示了在日本人口稠密的城市中检测到变化的区域的可能性。

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