*4.4. Simulated-Data Experiment*

Tables 2 and 3 show the RMSE and SRE values, respectively, of estimated abundances from the compared algorithms. The proposed J-LASU algorithm achieved better RMSE for all the simulated data. For the same level of SNR, J-LASU performed better than CLSUnSAL and SUnSAL-TV as well as ADSpLRU. The improvement also can be clearly seen in the *DS* data set from Figure 8. J-LASU preserved the square regions better than the others. Compared with the TV results, difference can be recognized in the small square regions in which J-LASU reconstructed the squares better. For the *FR* data sets, visually, the ADSpLRU abundance maps showed the most similar intensity with the corresponding true abundance maps. However, J-LASU is superior in preserving the gradation of intensity from edge to center of an abundance region, which is the drawback of the ADSpLRU. Compared with SUnSAL-TV, J-LASU was more accurate in determining whether an abundance is an outlier or just a low-intensity edge abundance. In addition, SUnSAL-TV produced stronger smoothing effects than J-LASU. In this case, J-LASU results are more similar with the true abundance map, which can easily be compared in the *FR2* data set.


**Table 2.** RMSE Comparison Result.

**Figure 8.** Estimated abundance maps for simulated data sets *DS* and *FR1–5* for SNR 30 dB (row **<sup>a</sup>**–**f**, respectively) using CLSUnSAL, SUnSAL-TV, ADSpLRU, and J-LASU (column **1–4**, respectively) compared to the true abundance (column **5**).


**Table 3.** SRE Comparison Result.
