Fig. 1. Spatial information in(a)the rectangular local window and(b)the irregular scale areas
Fig. 2. The flowchart of SIISA
Fig. 3. Multispectral images covering Rome, Italy,(a)RGB of multispectral image,(b)coarse image(S=8)
Fig. 4. Hyperspectral images covering University of Pavia, Italy,(a)RGB of hyperspectral image,(b)coarse image()
Fig. 5. Hyperspectral images covering Xiong'an New Area, China,(a)RGB of hyperspectral image,(b)coarse image()
Fig. 6. Mapping results,(a)reference image,(b)SSI,(c)PSSD,(d)OSI,(e)RWA,(f)SIISA
Fig. 7. Mapping results,(a)reference image,(b)SSI,(c)PSSD,(d)OSI,(e)RWA,(f)SIISA
Fig. 8. Mapping results,(a)reference image,(b)SSI,(c)PSSD,(d)OSI,(e)RWA,(f)SIISA
Fig. 9. Salient region,(a)reference image,(b)SSI,(c)PSSD,(d)OSI,(e)RWA, and(f)SIISA
Fig. 10. Values of(a)OA(%)and(b)Kappa obtained using the five different sub-pixel methods under different values of S
Fig. 11. OA(%)value of the SIISA in relation to weight parameter in(a)experiments 2 and(b)3
Fig. 12. OA(%)value of the SIISA in relation to segmentation scale parameter V in(a)experiments 2 and(b)3
Class | SSI | PSSD | OSI | RWA | SIISA |
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Vegetation(%) | 66.99 | 69.28 | 71.20 | 73.39 | 74.88 | Building(%) | 76.06 | 74.27 | 78.26 | 80.72 | 84.31 | Soil(%) | 61.66 | 64.45 | 67.44 | 69.64 | 71.37 | OA(%) | 70.10 | 71.45 | 73.73 | 76.05 | 78.67 | Kappa | 0.525 0 | 0.545 5 | 0.583 0 | 0.622 5 | 0.656 6 |
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Table 1. Accuracy evaluation of the five methods
Class | SSI | PSSD | OSI | RWA | SIISA |
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Shadow(%) | 49.23 | 55.56 | 57.44 | 60.29 | 61.94 | Water(%) | 96.85 | 96.68 | 97.01 | 97.28 | 97.77 | Road(%) | 64.99 | 62.08 | 68.64 | 70.37 | 78.39 | Tree(%) | 75.11 | 75.96 | 78.70 | 80.25 | 84.04 | Grass(%) | 71.00 | 74.33 | 75.49 | 78.39 | 79.87 | Rooftop(%) | 76.32 | 78.87 | 80.59 | 83.06 | 83.62 | OA(%) | 77.94 | 78.88 | 81.15 | 83.19 | 85.22 | Kappa | 0.726 5 | 0.738 7 | 0.765 9 | 0.797 7 | 0.815 7 |
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Table 2. Accuracy evaluation of the five methods