历史解译知识引导下组合遥感图谱特征的变化检测方法

A Remote Sensing Change Detection Method Combining with Spatial-Spectral Features under the Guidance of Historical Interpretation Knowledge

高空间分辨率遥感影像为地表变化监测提供了大量细节信息,这使得基于高分辨率影像的变化检测技术成为当前遥感领域的研究热点之一。本文提出了一种历史解译知识引导下组合遥感图谱特征的变化检测方法。首先,通过分割前后时相的组合影像构建空间位置一致的对象,并在提取对象光谱和纹理特征后,引入前期土地覆盖专题图指导2类图谱特征相似度的DS证据融合;然后,利用其历史存档图斑所属区域的优势地类标签指示不同特征相似度的证据差异融合;最后,基于GMM(Gaussian Mixture Mode)模型的二值化方法提取最终的变化区域。实验结果表明,该方法能充分利用历史解译知识指导不同时相高分辨率影像对象特征相似度组合,一定程度上提高了变化检测正确率。

Remote sensing images with high-spatial-resolution can provide a great amount of detailed information for land resource monitoring, which makes the change detection using the high-spatial-resolution remote sensing images become the focus of current remote sensing research. This article proposes a remote sensing change detection method through combining with the spatialspectral features under the guidance of historical interpretation knowledge. Firstly, the objects of consistent spatial position are constructed by segmenting the combined image from different phases. After that, the objects＇ spectral and texture features are extracted. Then, we introduce the previous land cover maps to guide the DS evidence fusion for the similarities of two different types of features. The archived class-type label in each polygon from the land cover maps is used to direct the similarity fusion with different evidence confidences. Finally, a binarization method based on the Gaussian Mixture Model is adopted to extract the change regions. Experimental results show that this method can take advantage of the historical interpretation knowledge to guide the fusion of different object- feature- similarities; moreover, to a certain extent, to effectively improve the accuracy of change detection.