Fig. 1. Framework of image mosaicking algorithm

Fig. 2. Schematic of calculation method of weighted sum method

Fig. 3. Schematic of splitting high and low status data

Fig. 4. Storage way of BIL

Fig. 5. Hyperspectral images to be mosaicked. (a) Image 1; (b) image 2

Fig. 6. Feature points of two hyperspectral images

Fig. 7. Range of matching feature points

Fig. 8. Correspondence between characteristic point pairs

Fig. 9. Image extracted in single channel. (a) Image 1; (b) image 2

Fig. 10. Mosaicking results of different images. (a) Low status image; (b) high status image

Fig. 11. Data of different types. (a) High status data; (b) low status data; (c) hyperspectral image data

Fig. 12. Stored data

Fig. 13. Image after mosaicking

Fig. 14. Two other hyperspectral images to be mosaicked. (a) Image 3; (b) image 4

Fig. 15. Hyperspectral image after mosaicking

Fig. 16. Similarity curves of different scene images. (a) Grass land; (b) cement land; (c) road; (d) forest land