  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.
