Automatic change detection by evidential fusion of change indices
Automatic change detection by evidential fusion of change indices
复制标题
DOI：
10.1016/j.rse.2004.04.001
复制
发表时间：
2004-06
影响因子：
13.5
通讯作者：
S. L. Hégarat-Mascle;R. Seltz
中科院分区：
文献类型：
--
作者：
S. L. Hégarat-Mascle;R. Seltz
探测影响大陆表面的变化在水文、气象和气候模拟中有着重要的应用。我们提出了一种通过融合多指标变化检测结果来改进单指标变化检测的方法。这种融合是在Dempster-Shafer证据理论的框架内进行的，该理论特别适合于“无变化”/“变化”类边界的不精确和无知的表示。根据所考虑的变更检测索引，我们还需要确定类的数量和特征。这是使用反向理论方法而不是经典的统计检验来完成的。将该算法应用于基于归一化差值、纹理演化和互信息（MI）三种常用变化指标的森林火灾损害评价。我们发现，由于这些指标的互补性，变化指标融合在降低虚警和误检水平方面都是有效的。
The detection of changes affecting continental surfaces has important applications in hydrological, meteorological, and climatic modeling. We propose a way to improve mono-index change detection by a fusion of multi-index change detection results. This fusion is performed in the framework of the Dempster-Shafer evidence theory, which is particularly suited to the representation of imprecision and ignorance at the “no change”/“change” class border. Depending on the change detection index considered, we also need to determine the class number and features. This is done using the a contrario theory approach rather than classical statistical tests. The proposed algorithm is applied to forest fire damage evaluation based on three popular change indices: normalized difference values, texture evolution, and mutual information (MI). We find that change index fusion is effective at reducing both false alarm and misdetection levels, due to the complementary nature of these indices.