Patch Similarity Graph Matrix-Based Unsupervised Remote Sensing Change Detection With Homogeneous and Heterogeneous Sensors

Change detection (CD) of remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields. In particular, one of the main challenges is to detect changes between heterogeneous images, where the difference in imaging mechanism makes it difficult to carry out a direct comparison. In this article, we propose an unsupervised CD framework based on the patch similarity graph matrix (PSGM), which assumes that the patch similarity graph structure of each homogeneous or heterogeneous image is consistent if no change occurs. First, it learns the PSGM of one image based on the self-expressive property, which can be interpreted as containing the edges of the fully connected graphs with each image patch as a vertex. Then, the change level depends on how much one image still conforms to the similarity graph structure learned from the other image. Meanwhile, the change map can be further optimized by using the prior sparse knowledge that only a small part of the image changed and most areas remain unchanged. Experiments with both homogeneous and heterogeneous data sets demonstrate the effective performance of the proposed PSGM-based CD method.

Automated summary

The article proposes an unsupervised CD framework based on PSGM, learning the PSGM of each image and determining change levels. Experimental results demonstrate the effectiveness of the method for both homogeneous and heterogeneous datasets.