# A Joint Morphological Profiles and Patch Tensor Change Detection for Hyperspectral Imagery

arXiv: https://arxiv.org/abs/2201.08027 · HTML: https://mlsanity.com/paper/2201.08027/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)

## Built on by (most cited first, 1 of 1)

- [A spectral-spatial fusion anomaly detection method for hyperspectral imagery](https://mlsanity.com/paper/2202.11889/) (2022)

## Abstract (arXiv)

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.