# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 3 Numerical Experiments  
The numerical experiments are performed with an Intel Core I7-6500U 2.50 GHz, 8 GB RAM, and the following benchmark datasets are analyzed:  
* _Herniston_ dataset (The dataset can be downloaded at: [[https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/Herniston](https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/Herniston)]([https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/Herniston](https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/Herniston)) access date 15 August 2022) consists of two coregistered HS images over the city of Hermiston, Oregon, in 2004 and in 2007. The images are acquired by the Hyperion sensor and consist of \(390\times 200\) pixels with 242 spectral bands; see Figure 1 for the RGB rendering of this dataset. * _USA_ dataset (The dataset can be downloaded at: [[https://rslab.ut.ac.ir/documents/81](https://rslab.ut.ac.ir/documents/81)]([https://rslab.ut.ac.ir/documents/81](https://rslab.ut.ac.ir/documents/81)) 960329/82034892/Hyperspectral_Change_Datasets.zip access date 15 August 2022) consists again of two HS images acquired by the Hyperion sensor over Hermiston in Umatilla County, on 1 May 2004, and 8 May 2007, respectively. The land cover types are soil, irrigated fields, river, building, types of cultivated land, and grassland. These two HS images consist of \(307\times 241\) pixels and \(154\) spectral bands; see Figure 2 for the RGB rendering of this dataset. * _River_ (The dataset can be downloaded at: [[http://crabwq.github.io](http://crabwq.github.io)]([http://crabwq.github.io](http://crabwq.github.io)) access date 15 August 2022) dataset [35] contains two HS images taken in Jiangsu Province, China, acquired on 3 May 2013 and on 31 December 2013, respectively, by the (EO-1) Hyperion sensor; see Figure 3 for the RGB rendering of this dataset. These two images consist of \(463\times 241\) pixels with only 198 spectral bands after noise removal. * _Bay Area_ (The dataset can be downloaded at [[https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/bayArea](https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/bayArea)]([https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/bayArea](https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset/-/tree/master/bayArea)) access date 15 August 2022) dataset consists of two HS images taken in the years 2013 and 2015 with the AVIRIS sensor surrounding the city of Patterson (California). The spatial dimension is \(600\times 500\) pixels and there are 224 spectral bands. In the available ground truth, 75.79% of the total number of pixels are not classified, i.e., do not have any label. These images were coregistered by using the HypeRvieW desktop tool; see [36; 37] and Figure 4 for the RGB rendering. The choice of these datasets is not casual as they can be considered typical benchmarks to assess the accuracy of the CD algorithm for three different cases. Indeed, in the _Hermiston_ ground truth, in Figure 5g and, the changed areas have a very clear and neat silhouette; in the _USA_ dataset ground truth, in Figure 6g, the changed areas have both neat profiles as well as fragmented and very thin boundaries. Finally, in the ground truth of the _River_ dataset, in Figure 7g, the majority of the changed areas look very fragmented and no clear shapes can be highlighted. Regarding the _Bay Area_ ground-truth, Figure 8h, the same observation as in the _Hermiston_ case hold. The goal of change detection algorithms is to provide a method that would achieve good accuracy regardless of the shape of the changed areas; hence, it is important to test the proposed methodology at least on these three different types of scenarios. In all four cases, the RBG rendering, produced by applying the algorithm in [38], is performed only for visualization purposes. Figure 4: RGB rendering of the _Bay Area_ dataset scenes.