# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 5 Discussion  
In this section, we compare the obtained accuracy results with other methods available in the literature, whose results have been published. For the _Herniston_ dataset, in Table 3, the achieved results produced by other methods are reported. In particular, in the table, for each method, a reference in which the reported results are published is provided. The method AICA [51] achieves the best OA, but there is no automatic selection of the best output, while the method proposed here obtains the third best OA. Among the methods that provide also the \(K\) values, our method, RSB, yields the highest value. The considered competitors are the following: AI-QLP [49], an approximate iterative QLP decomposition; the method in [10], which also works for multi-class change detection classification; BIC2 [17], a methodology based on iterative clustering; ORCHESTRA [50], an autoencoder-based approach; GETNET [35], a method based on an end-to-end 2D convolution neural network; CVA [19], an approach that exploits change vector analysis; PCA-CVA [52], enhanced PCA, and inverse triangular function-based thresholding, which are used together with change vector analysis; CNN [23], an approach based on autoencoder, clustering, and pseudo-label generation; CUDA [20], a method that exploits similarity measures, PCA, and the automatic Otsu thresholding; and SFBS-FFGNET [18], a CNN framework with slow-fast band selection and feature fusion. Regarding the _USA_ dataset, the results are reported in Table 4, and again, a reference is provided for the published results. Here, BIC2 [17] achieves the best OA, but again, there is no automatic selection of the best scenario, while the best value for \(K\) is provided by the method SSCNN-S [24], a spectral-spatial convolutional neural network approach realized with a Siamese architecture. Among all the proposed approaches, the method here presented, RSB, achieves good accuracy (OA = 0.9579), which is behind only advanced methods based on iterative learning and ad hoc neural network architectures, and it also yields the third best K value (K = 0.8781). The analyzed competitors are GETNET-1 and GETNET-2, which denote an end-to-end 2D convolutional neural network without unmixing and with unmixing, respectively; CVA [19], MAD-SVM, and the multivariate alteration detection method (MAD [53]), where the thresholding operation is carried out via SVM; IR-MAD-SVM, the iteratively reweighted MAD [54; 55], where, again, the thresholding selection is done via SVM; the method proposed in [56], which is based on 3D convolution deep learning; BIC2 [17]; AICA [51]; HybridSN [57], a hybrid spectral CNN; SCNN-S [24] and SSCNN-S [24], which denote a spectral convolutional NN with a Siamese architecture and a spectral-spatial convolutional NN with a Siamese architecture, respectively; TRIFCD-MS [7] and TRIFCD-Fusion [7], methods based on the simultaneous fusion of low-spatial-resolution HS images and low-spectral-resolution MS images with the use of a tensor regression; and BCG-Net [58], an architecture driven by a partial Siamese \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Herniston**} \\  & **OA** & **K** \\ \hline RSB+PP & 0.9876 & **0.9447** \\ AI-QLP [49] & 0.9856 & 0.9400 \\ Method [10] & 0.9874 & – \\ BIC2 [17] & 0.9881 & – \\ ORCHESTRA [50] & 0.9871 & – \\ GETNET [18] & 0.9540 & 0.7810 \\ CVA [18] & 0.9390 & 0.6610 \\ PCA-CVA [18] & 0.9790 & 0.9060 \\ CNN [18] & 0.9470 & 0.7600 \\ CUDA [20] & 0.9848 & – \\ AICA [51] & **0.9887** & – \\ SFBS-FFGNET [18] & 0.9850 & 0.9310 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The proposed approach, RSB, with post-processing, and other competitors on the _Herniston_ dataset. The best results are displayed in bold. united unmixing module, together with multi-temporal spectral unmixing and a temporal correlation constraint.