Coastal Zone Classification Based on Multisource Remote Sensing Imagery Fusion

Abstract

The main objective of this paper was to assess the capability of multisource remote sensing imagery fusion for coastal zone classification. Five scenes of Gaofen- (GF-) 1 optic imagery and four scenes of synthetic aperture radar (SAR) (C-band Sentinel-1 and L-band ALOS-2) imagery were collected and matched. Note that GF-1 is the first satellite of the China high-resolution earth observation system, which acquires multispectral data with decametric spatial resolution, high temporal resolution, and wide coverage. The results showed that based on the comparison of C- and L-band SAR for coastal coverage, it is verified that C band is superior to L band and parameter subsets of σvv0, σvh0, and Dcross can be effectively used for coastal classification. A new fusion method based on the wavelet transform (WT) was also proposed and used for imagery fusion. Statistical values for the mean, entropy, gradient, and correlation coefficient of the proposed method were 67.526, 7.321, 6.440, and 0.955, respectively. We therefore conclude that the result of our proposed method is superior to GF-1 imagery and traditional HIS fusion results. Finally, the classification output was determined along with an assessment of classification accuracy and kappa coefficient. The kappa coefficient and overall accuracy of the classification were 0.8236 and 85.9774%, respectively, so the proposed fusion method had a satisfying performance for coastal coverage mapping.