Back to Search
Start Over
Cross-Modal feature description for remote sensing image matching
- Source :
- International Journal of Applied Earth Observations and Geoinformation, Vol 112, Iss , Pp 102964- (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- Effective feature description for cross-modal remote sensing matching is challenging due to the complex geometric and radiometric differences between multimodal images. Currently, Siamese or pseudo-Siamese networks directly describe features from multimodal remote sensing images at the fully connected layer, however, the similarity of cross-modal features during feature extraction is barely considered. Therefore, we construct a cross-modal feature description matching network (CM-Net) for remote sensing image matching in this paper. First, a contextual self-attention module is proposed to add semantic global dependency information using candidate and non-candidate keypoint patches. Then, a cross-fusion module is designed to obtain cross-modal feature descriptions through information interaction. Finally, a similarity matching loss function is presented to optimize discriminative feature representations, converting a matching task into a classification task. The proposed CM-Net model is evaluated by qualitative and quantitative experiments on four multimodal image datasets, which achieves the average Matching score (M.S.), Mean Matching Accuracy (MMA), and average Root-mean-square error (aRMSE) of 0.781, 0.275, and 1.726, respectively. The comparative study demonstrates the superior performance of the proposed CM-Net for the remote sensing image matching.
Details
- Language :
- English
- ISSN :
- 15698432
- Volume :
- 112
- Issue :
- 102964-
- Database :
- Directory of Open Access Journals
- Journal :
- International Journal of Applied Earth Observations and Geoinformation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b5d57e2528e64d78b31e0e7a1678e1c0
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.jag.2022.102964