Integration of Optical and SAR Imagery for Dual PolSAR Features Optimization and Land Cover Mapping

Abstract

Integrating optical and synthetic aperture radar (SAR) data at a feature level has proven to be a powerful method for land cover mapping. However, blurring effects and resolution loss induced by the despeckling procedure can degrade the estimation accuracy of SAR features and therefore introduce artifacts into data fusion. This article presents a method to solve this problem using SAR features estimation driven by both optical and dual polarimetric SAR images. The incentive behind this method is that compared to a low signal-to-noise ratio (SNR) SAR image, labeling sample from the optical image with higher SNR and spectral resolution can enhance sample homogeneity. The homogeneous sample following the same population can ensure SAR feature estimation accuracy and simultaneously preserve the spatial resolution. The optimum SAR features, together with optical images, are then integrated using the machine learning algorithm to obtain a classification map. We further fully evaluate the influence of SAR features estimation on fusion classification. The effectiveness of the proposed method is validated by moderate resolution Sentinel-1/2 datasets over the Saint Louis area, where abundant land cover types and rich texture present a relative challenge for classification. The quantitative analysis indicates that the proposed method can increase around 5%–10% in overall accuracy than the state-of-the-art methods. The results confirm the necessity of SAR features with high quality for land cover classification, especially over the textural region.