Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.

Evaluation of Microwave-Optical-Infrared Satellite Imagery for Land Cover Mapping

Satellite images are vital tool in various applications like land use, land cover mapping and geographic information system (GIS) etc. A variety of factors involved in the process of image acquisition, introduce geometric distortions, which are removed by pre-processing of the digital imagery. Geometric correction is the process of rectification of geometric errors introduced in the imagery during the process of its acquisition. From practical point of view, the Sentinel-1 images are to be depended as source of microwave satellite imagery. While, Sentinel-2 are to be used for providing the study with the required visible-infrared images. The study includes performing different digital image processing and analysis techniques, such as: geometric and radiometric corrections, spatial merge (fusion), feature extraction with using different spatial filtering techniques and spectral classification to reveal which LULC image presents better accuracy results. The microwave portion of the spectrum covers the range from approximately 1cm to 1m in wavelength. Because of their long wavelengths, compared to the visible and infrared, microwaves have special properties that are important for remote sensing. Longer wavelength microwave radiation can penetrate through cloud cover, haze, dust, and all but the heaviest rainfall as the longer wavelengths are not susceptible to atmospheric scattering which affects shorter optical wavelengths. This property allows detection of microwave energy under almost all weather and environmental conditions so that data can be collected at any time.