*4.5. Real-Data Experiment*

The visual comparisons among the five sparse unmixing algorithms and the mineral maps for the Cuprite data can be observed in Figure 9. The images in the first column show the comparison for *alunite* abundance maps. Among the results of the compared algorithms, The proposed J-LASU produced the map that was the most similar to the mineral map, with less outliers found in the lower-left side of the map. The same superiority was also found among the *chalcedony* and *kaolinite* abundance maps in the second and third columns, respectively. Compared to SUnSAL-TV, J-LASU had less outliers or lower intensity of outliers, most of which were found on the left-side region of the maps.

It should be noted that the estimated abundance maps of any sparse unmixing algorithm are not exactly the same as the mineral maps generated from the Tricorder software in terms of intensity. The software produced the pixel-level classification maps, while the sparse unmixing algorithms executed subpixel-level classification. However, the comparison of outliers in this paper refers to the abundances that no longer exist in the mineral map. Overall, J-LASU estimated abundance maps had smooth gradation of intensity from the edge of a detected region to the center, and removed tiny regions that were found in the other algorithms' map, which seems to be the outliers in J-LASU algorithm.

For the Urban data, Figure 10 shows the ground truth and abundance maps of the four endmembers estimated by the compared algorithms. J-LASU algorithm resulted in the most similar maps to the ground truth, especially for the *asphalt* abundance map which is easier to be compared with those of the other algorithms. The quantitative comparisons also show that J-LASU yielded the best performance, with the highest SRE and lowest RMSE, as shown in Table 4. Compare to the simulated data, the Urban data experienced relatively high RMSEs for all compared algorithms. This is due to the fact that the ground truth abudance maps used for the Urban data are not achieved from a ground measurement, but from a method in which error possibly exists in term of method accuration.

**Figure 9.** Estimated abundance maps of Cuprite data subscene for endmember *alunite, chalcedony*, and *kaolinite* (column **1**–**3**) using CLSUnSAL, SUnSAL-TV, ADSpLRU and J-LASU (row **b**–**<sup>e</sup>**). First row (**a**) shows classification maps of endmembers from USGS Tetracorder.

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**Figure 10.** Estimated abundance maps of Urban data subscene for endmember *asphalt, grass, tree,* and *roof* (column **1**–**4**) using CLSUnSAL, SUnSAL-TV, ADSpLRU and J-LASU (row **b**–**<sup>e</sup>**). First row (**a**) shows the ground truth abundance maps.


**Table 4.** RMSE and SRE Comparison Result for Urban data.
