## 4 Results  
In this section, we present illustrative results of the operation of the registration algorithm with several types of remotely sensing data. Specifically, we provide experimental results for multitemporal and multisensorial image registration. So, the proposed algorithms have been tested in a large set of satellite images. Here we present a subset that illustrates some of their relevant features. The images have been obtained in the Canary Islands-Azores-Gibraltar area (39 deg15'N-19 deg15'W; 26 deg15'N-6 deg15'W), known as CANIGO area [19]. Figure 6(a) shows the results obtained for the multitemporal NOAA-AVHRR images of Figures 3(a) and (b), corresponding to sea surface temperature data. Qualitatively, the correspondence between the reference and sensed images obtained by the proposed algorithms is accurate to within a pixel error in average. Figure 6(b) presents the results of the proposed method in multisensorial images, corresponding to sea surface temperature and ocean colour data, acquired by the AVHRR and SeaWiFS sensors, respectively. Finally, Figure 6 (c) presents the results of the proposed method in multisensorial SST images, corresponding to AQUA-MODIS and ERS/2-ATSR images of the Canary Island area, respectively. The correspondence between the reference and input images shows that the registration obtained by the proposed algorithm is accurate to within a pixel error in average. Figure 6: Results of contour-based approach applied to multitemporal/multisensorial image registration: **(a)** Multitemporal SST images from NOAA-AVHRR sensor, **(b)** multisensorial SST and ocean color images from AVHRR and SeaWiFS sensors, and **(c)** multisensorial SST images from AQUA-MODIS and ERS/2-ATSR sensors. Table 2 shows the results obtained in a subset of multitemporal and multisensorial images. In it, the captured data and the initial/final energy obtained in the minimization process are presented for the proposed algorithm. From the perspective of the analysis and extraction of conclusions, a particular case is presented which gives an idea of the limits of the proposed technique. It corresponds to the comparison between the proposed optimization techniques with the area-based approaches, as implemented in [11]. Results are presented in Table 3 detailing the number of points used in the transformation parameters estimation (n) and the total RMS error in the horizontal (x) and vertical (y) directions. The proposed algorithm outperforms area-based techniques by over half a pixel on the average, even when a large number of control points are available. In consequence, this algorithm is adequate for automatic satellite imagery registration, even when they have partial or total occlusions and, thus, it is difficult to obtain a large number of well-distributed CPs. \begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Images Satellite** & **Initial Energy** & **Final Energy** & **R.M.S.** \\ \hline _Reference_: 18 January 1998 _Satellite_: NOAA/AVHRR-3 (517\(\times\)993) _Sensed_: 3 July 1998 _Satellite_: NOAA/AVHRR-3 (237\(\times\)629) _Reference_: 24 April 1999 _Satellite_: SeaStar/SeaWiFs (535\(\times\)950) _Sensed_: 24 April 1999 _Satellite_: NOAA/AVHRR-3 (535\(\times\)950) _Reference_: 15 August 2001 _Satellite_: AQUA/MODIS (500\(\times\)1100) _Sensed_: 15 August 2001 _Satellite_: ERS2-ATSR (500\(\times\)500) & -0.0466 _S_: 0.0793 _Satellite_: A[0.500\(\times\)500) & -0.0466 _S_: 0.0793 _Satellite_: A[0.500\(\times\)1100) _Sensed_: 15 August 2001 _Satellite_: ERS2-ATSR (500\(\times\)500) & -0.0869 _S_: 1.184 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of satellite image registration for three test cases.