A novel classifier for improving wetland mapping by integrating image fusion techniques and ensemble machine learning classifiers

Abstract Among all the natural features, wetlands are the most difficult to map and monitor because of their complicated morphology, i.e., their diverse shapes and sizes, ranging from open waterlogged regions with sparse vegetation to thickly wooded areas with a distant distribution pattern. Wetland mapping has been performed with optical remote sensing data for a long time; but the accuracy varies with time and space owing to the complexity of wetland systems. Therefore, the objective of this work is to examine the efficacy of a novel and unique approach to automated complex wetland mapping in Bangladesh's Sunamganj District, using combined image fusion with machine learning techniques. The image fusion technique was used to fuse the multispectral bands (MS) of Landsat 8 OLI (bands 5, 4, 3) with the panchromatic band of Landsat 8, (band 8). The quality of the fused images was determined by calculating the correlation between the spectral values of the original multispectral and fused images, as well as through edge detection techniques. In addition to this, four advanced machine learning classifiers, i.e. random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT), were used to classify the complex wetland image. Furthermore, a hybrid classifier was created by applying an artificial neural network (ANN) to previously identified models and spectral bands of satellite images. To validate the identified wetland maps, the Kappa coefficient and root mean square error (RMSE) have been used. The result shows that in wetland mapping, the Brovey image fusion approach surpassed the Gram-Schmidt image fusion technique. In addition, the accuracy of all four machine learning classifiers was higher on the fused images than on the MS image. The proposed fused ANN-based hybrid model outperformed all fused and MS classification models in terms of accuracy (kappa: 89.4% and RMSE: 0.13). According to the findings of this study, image-fusion based machine learning classifiers could be used for wetland mapping in other regions of the world, as well as for mapping river dynamics, forest cover, urban growth, and croplands.