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.
翻译:多时超光谱图像可用于探测已逐渐引起研究人员注意的已发生变化的信息,然而,传统的变化检测算法并未深入探讨空间和光谱已发生变化的特征的相关性,从而导致检测准确性低;为了更好地挖掘已变化特征的光谱和空间信息,提议采用一种组合形态和补丁色色色变色探测法(JMPT),最初,采用基于补丁的强光谱战略来利用空间结构的类似属性,即非重叠的本地补丁图像被重塑为一个新的抗拉立方体,然后采用三阶塔克脱光谱和图像重建战略以获得更强有力的多时超光谱数据集;同时,采用多种形态特征特征,包括最大树和小树,以提取多时光谱图像的不同属性;最后,这些结果与最后变化检测图相结合。在两个真正的超光谱数据集上进行的实验表明,拟议的探测器能够取得更好的检测性能。