论文标题
高光谱图像的关节形态曲线和贴片张量变化检测
A Joint Morphological Profiles and Patch Tensor Change Detection for Hyperspectral Imagery
论文作者
论文摘要
多节超光谱图像可用于检测变化的信息，这逐渐吸引了研究人员的注意力。但是，传统的变化检测算法并未深入探讨空间和光谱变化特征的相关性，从而导致检测准确性较低。为了更好地挖掘变化特征的光谱和空间信息，提出了联合形态和贴片调整变化检测（JMPT）方法。最初，采用了基于贴片的张量策略来利用空间结构的类似属性，在该策略中，非重叠的本地贴片图像被重塑为新的张量立方体，然后采用了三阶Tucker Demoppositon和图像重建策略来获得更强大的多型多阶段性低体光谱数据集。同时，应用多个形态学曲线（包括max-Tree和Min-Tree）用于提取多颞图像的不同属性。最后，将这些结果融合为一般最终更改图图。在两个实际的高光谱数据集上进行的实验表明，所提出的检测器可以实现更好的检测性能。
Multi-temporal hyperspectral images can be used to detect changed information, which has gradually attracted researchers' attention. However, traditional change detection algorithms have not deeply explored the relevance of spatial and spectral changed features, which leads to low detection accuracy. To better excavate both spectral and spatial information of changed features, a joint morphology and patch-tensor change detection (JMPT) method is proposed. Initially, a patch-based tensor strategy is adopted to exploit similar property of spatial structure, where the non-overlapping local patch image is reshaped into a new tensor cube, and then three-order Tucker decompositon and image reconstruction strategies are adopted to obtain more robust multi-temporal hyperspectral datasets. Meanwhile, multiple morphological profiles including max-tree and min-tree are applied to extract different attributes of multi-temporal images. Finally, these results are fused to general a final change detection map. Experiments conducted on two real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.
