Coarse-to-fine matching via cross fusion of satellite images

Coarse-to-fine matching via cross fusion of satellite images

Blog Article

The registration of multimodal satellite images is essential for a prerequisite for accruing complementary observational data.Nevertheless, the differential imaging nuances amongst non-linear radiometric multimodal images precipitate a complexity in keypoint detection, rendering it a great challenge.This complexity exacerbates the difficulty encountered in matching multimodal satellite images.

In this paper, a dual-branch cross fusion network (DF-Net) is proposed for the purpose of satellite image registration.DF-Net relies on the self-attention granted to a pair 09gi shades eq of images, thereby providing cross-modal fusion feature descriptions.Initially, reference and sensed images are deployed as inputs for the dual-branch network, which in turn engenders feature descriptions of both high and low resolution, respectively.

Sequentially, the matching of individual feature descriptions is anchored on the low-resolution feature map, paving the way for the establishment of coarse matching correspondences.Subsequently, the outcomes of these coarse correspondences are transposed onto the feature map with a higher resolution, thereby generating fine matching results for each coarse correspondence.An exhaustive set of qualitative and quantitative assessments have been administered on three satellite image datasets encompassing a diverse range of scenarios.

The average fig leaf apron Repeatability (Rep.), Mean Matching Accuracy (MMA), and Root-Mean-Square Error (RMSE) of the DF-Net applied to three large-scale satellite images were recorded to be 0.71, 0.

65, and 2.34, respectively.These findings buttress the proficiency of the proposed strategy in facilitating cross-modal matching and bear testimony to the sterling performance of the method proposed.