Dual Graph Learning Affinity Propagation for Multimodal Remote Sensing Image Clustering

Multimodal remote sensing image recognition aims to identify a category of land cover for every pixel with consistency and complementary information provided by different modalities. Most existing methods perform land cover recognition in a supervised manner with explicit label guidance. It is challenging to perform recognition without label guidance due to the complex spatial distribution and modality incompatibility, especially for large-scale data. In this article, we propose a dual graph learning affinity propagation (DGLAP) method for multimodal remote sensing image clustering. Based on the consistent spatial distribution from local regions, the proposed method learns an
$N \times M$
consensus anchor graph from N denoised pixels and M anchors by adaptive weighting different modalities along with projection learning. Meanwhile, an optimal
$M \times M$
compressed consensus anchor graph is learned from the updated anchors in different modalities with diverse adaptive contributions and connectivity constraint. Since
$M \ll N$
, clustering results can be efficiently obtained according to affinity propagation from the pseudolabeled anchors to the pixels without additional steps. An alternating optimization algorithm is devised to solve the proposed formulation. This is the first attempt to propose a ultraefficient graph-based clustering method with linear time complexity
$\mathcal {O}(N)$
and low time cost for large-scale multimodal remote sensing data. Extensive experiments on three datasets demonstrate the superiority of the proposed method over the state-of-the-art methods in both efficacy and efficiency. The code is released at
https://github.com/ZhangYongshan/DGLAP
.