Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images

Abstract

The Unmanned Aerial Vehicle (UAV) and Google Earth (GE) RGB images have ultra-high spatial resolution. But it is difficult to get a high classification accuracy due to the poor spectral resolution. In this article, the object-based wetland classification is investigated using multi-feature combination of ultra-high spatial resolution multispectral images (MSI). A Gram-Schmidt (GS) transformation is used to fuze Sentinel-2A data with UAV and GE RGB images, respectively, in order to obtain the ultra-high spatial resolution MSI as data sources. Three different feature combination classification scenarios are constructed for fusion GE and UAV MSI, respectively, based on selected features. The object-based random forest (RF) algorithms with parameters (mtry and ntree) optimization are used to carry out finer wetland classification. Results show that the fusion GE and UAV MSI have good applicability in the finer wetland classification, especially the fusion UAV images, and integrating multi-source features could improve classification accuracy. Both data sources reach the highest accuracy in scenario3. The overall accuracy of fusion UAV image scenario3 is 94.31% (Kappa = 0.9353), and that of fusion GE image scenario3 is 87.37% (Kappa = 0.8528). The contribution of different features to wetland classification is obtained with spectral and vegetation indexes, texture, geometric and contextual features.