Fig. 1. Framework of proposed remote sensing image mosaic algorithm

Fig. 2. Diagram of bidirectional mutual selection matching

Fig. 3. Weighted fusion process

Fig. 4. Response curve of human visual perception

Fig. 5. Plant growth function curve

Fig. 6. Function curve of weighting factor ω₁

Fig. 7. Weighted fusion process of improved S-model

Fig. 8. Preprocessing of remote sensing image. (a) Original image;(b) mean filtering; (c) guided filtering

Fig. 9. Comparison of feature point matching strategies. (a) One-way matching; (b) bidirectional mutual selection matching; (c) partial enlargement of Fig. 9(a); (d) partial enlargement of Fig. 9(b)

Fig. 10. Comparison of matching accuracy for images

Fig. 11. Comparison of three fusion strategies for images of industrial_area. (a)(b) Original images; (c) S-type fusion; (d) direct average fusion; (e) gradual integration

Fig. 12. Comparison of three fusion strategies for images of intersection. (a)(b) Original images; (c) S-type fusion; (d) direct average fusion; (e) gradual integration

Fig. 13. Comparison of three fusion strategies for images of harbor. (a)(b) Original images; (c) S-type fusion; (d) direct average fusion; (e) gradual integration

Fig. 14. Comparison of three fusion strategies for remote sensing images obtained from Gaofen-1 satellite with resolution of 2 m/8 m. (a)(b) Original images; (c) S-type fusion; (d) direct average fusion; (e) gradual integration

Fig. 15. Comparison of three fusion strategies for remote sensing images obtained from Gaofen-1 satellite with resolution of 16 m. (a)(b) Original images; (c) S-type fusion; (d) direct average fusion; (e) gradual integration

Fig. 16. Comparison of image information entropy data