Evaluation and analysis of fusion algorithms for active and passive remote sensing image

In order to compare fusion algorithms considering both active and passive remotely sensed data, a few well-known techniques, including Brovey, Gram-Schmidt spectral sharpening (GS), Hue Saturation Value (HSV), Principal Component Analysis spectral sharpening (PCA), and à trous wavelet transform applied in the Hue Intensity Saturation space (ATWT+HIS) are compared with the simple joint analysis of the original SAR and optical images. Experiments are performed using pairs of ALOS ANVIR-2 and PALSAR, SPOT and PALSAR, Landsat TM and ERS data. In this paper, the above mentioned methods and dataset combinations are tested and compared by means of quantitative indexes such as entropy, average gradient (AG), correlation coefficient (CC), deviation index (DI) and classification accuracy. The results obtained demonstrate that classification accuracy values can be improved by using these approaches by as much as 10% with respect to the best achievable value using only optical and SAR data separately. By means of a detailed analysis of the relationship between classification accuracy and quantitative indexes usually considered to evaluate the value of the fused products, our experiments show that larger deviation index (DI) and smaller correlation coefficient (CC) values are usually connected to more accurate classification results.