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首页> 外文期刊>International journal of image and data fusion >Fusion of multispectral imagery and DSMs for building change detection using belief functions and reliabilities
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Fusion of multispectral imagery and DSMs for building change detection using belief functions and reliabilities

机译：融合多光谱图像和DSM以使用信念函数和可靠性进行建筑物变化检测

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The extraction of building changes from very high resolution satellite images is an important but challenging task in remote sensing. Digital Surface Models (DSMs) generated from stereo imagery have proved to be valuable additional data sources for this task. In order to efficiently use the change information from the DSMs and spectral images, belief functions have .been introduced. In this article, two-step building change detection fusion models based on both Dempster-Shafer Theory (DST) and Dezert-Smarandache Theory (DSmT) frameworks are proposed. In the first step, basic belief assignments (BBAs) of the change indicators from images and DSMs are calculated by using a refined sigmoidal BBA model. Then these BBAs are employed for the new proposed building change detection decision fusion approach. In order to cover the miss-detections introduced by the wrong height values of the DSMs and incomplete information from images, disparity maps from the DSM generation procedure and shadow maps from the multispectral channels are adopted to generate reliability maps, which are further integrated to the fusion models. In the last step, building change masks are generated based on four decision-making criteria. In the experimental part of this work, we evaluate the performance of this new building change detection method on real satellite images thanks to a building change reference mask representing the ground truth. Substantial accuracy improvements are achieved when comparing the new results with those obtained from classical 3D change detection approaches.
机译：从高分辨率卫星图像中提取建筑物变化是遥感中一项重要但具有挑战性的任务。事实证明，从立体图像生成的数字表面模型（DSM）对于此任务是有价值的附加数据源。为了有效地使用来自DSM和光谱图像的变化信息，引入了置信函数。本文提出了基于Dempster-Shafer理论（DST）和Dezert-Smarandache理论（DSmT）框架的两步建筑变化检测融合模型。第一步，使用改进的S型BBA模型计算来自图像和DSM的变化指标的基本信念分配（BBA）。然后将这些BBA用于新提议的建筑物变更检测决策融合方法。为了覆盖由错误的DSM高度值和来自图像的不完整信息引起的误检测，采用DSM生成过程中的视差图和多光谱通道中的阴影图来生成可靠性图，并将其进一步集成到融合模型。在最后一步中，将基于四个决策标准生成建筑变更模板。在这项工作的实验部分，我们借助于代表地面真实情况的建筑物变化参考遮罩，评估了这种新的建筑物变化检测方法在真实卫星图像上的性能。将新结果与经典3D变化检测方法获得的结果进行比较时，可以大大提高精度。

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