# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 6 Conclusions  
In the present paper, the method of RSB based on successive binarization techniques is proposed for the task of change detection in hyperspectral images. The method of RSB relies on the computation of specific dissimilarity measures and on successive different thresholding iterations, which make the produced output robust with respect to the chosen error function and fully automatic. RSB results in greater accuracy than commonly adopted thresholding strategies. Moreover, to improve the obtained accuracy, two post-processing techniques are applied according to the type of produced binary change map. The final obtained results are highly competitive compared to the most up-to-date methodologies.  
The research of [PERSON] is founded by PON Project AIM 1852414 CUP H95G18000120006 ATT1 and Project CUP H95F21001470001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** A public repository for downloading the code is available at [[https://github.com/[PERSON]/RSB](https://github.com/[PERSON]/RSB)]([https://github.com/[PERSON]/RSB](https://github.com/[PERSON]/RSB)) (accessed on 30 August 2022).  
**Acknowledgments:** The author thanks the Italian National Group for Scientific Computing (Gruppo Nazionale per il Calcolo Scientific) for its valuable support under the INDAM-GNCS project CUP_E55F22000270001.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Bay Area**} & \\  & **OA** & **K** \\ \hline RSB+PP & 0.9274 & 0.6540 \\ ORCHESTRA [50] & 0.9561 & – \\ EUC+EM [37] & 0.8884 & – \\ EUC + Otsu [37] & 0.8501 & – \\ SAM + Otsu [37] & 0.9493 & – \\ EUC + WAT + Otsu [37] & 0.8666 & – \\ SAM + WAT + EM [37] & 0.9437 & – \\ SAM + WAT + Otsu [37] & **0.9694** & – \\ AICA [50] & 0.8529 & – \\ BIC2 [17] & 0.9551 & – \\ \hline \hline \end{tabular}
\end{table}
Table 6: The proposed approach, RSB, with post-processing, and other competitors on the _Bay Area_ dataset. The best results are displayed in bold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**River**} \\  & **OA** & **K** \\ \hline RSB+PP & **0.9681** & **0.8014** \\ GETNET-1 [35] & 0.9497 & 0.7662 \\ GETNET-2 [35] & 0.9514 & 0.7539 \\ CVA [19] & 0.9529 & 0.7967 \\ PCA-CVA [52] & 0.9434 & 0.7326 \\ IR-MAD [54; 55] & 0.8963 & 0.6632 \\ SVM [59] & 0.9046 & 0.6360 \\ CNN [35] & 0.9440 & 0.6867 \\ HybridSN [57] & 0.9614 & 0.7371 \\ SCNN-S [24] & 0.9610 & 0.7300 \\ SSCNN-S [24] & 0.9640 & 0.7431 \\ TRIFCD-MS [7] & 0.9442 & 0.7063 \\ TRIFCD-Fusion [7] & 0.9529 & 0.7573 \\ SFBS-FFGNET [18] & 0.9670 & 0.7710 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The proposed approach, RSB, with post-processing, and other competitors on the _River_ dataset. The best results are displayed in bold.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.