Optical and Synthetic Aperture Radar (SAR) fusion is addressed in this paper. Intensity–Hue–Saturation (IHS) is an easily implemented fusion method and can separate Red–Green–Blue (RGB) images into three independent components; however, using this method directly for optical and SAR images fusion will cause spectral distortion. The Gradient Transfer Fusion (GTF) algorithm is proposed firstly for infrared and gray visible images fusion, which formulates image fusion as an optimization problem and keeps the radiation information and spatial details simultaneously. However, the algorithm assumes that the spatial details only come from one of the source images, which is inconsistent with the actual situation of optical and SAR images fusion. In this paper, a fusion algorithm named IHS-GTF for optical and SAR images is proposed, which combines the advantages of IHS and GTF and considers the spatial details from the both images based on pixel saliency. The proposed method was assessed by visual analysis and ten indices and was further tested by extracting impervious surface (IS) from the fused image with random forest classifier. The results show the good preservation of spatial details and spectral information by our proposed method, and the overall accuracy of IS extraction is 2% higher than that of using optical image alone. The results demonstrate the ability of the proposed method for fusing optical and SAR data effectively to generate useful data.

The fusion of optical and synthetic aperture radar (SAR) images is a promising method to extract urban impervious surface (IS) accurately. Previous studies have shown that the feature-level fusion of optical and SAR images can significantly improve IS extraction. However, they generally use simple layer stacking for features fusion, ignoring the interaction between optical and SAR images. Besides, most of the features they used are shallow features manually extracted, such as texture and geometric features, lacking the use of high-level semantic features of images. The lack of publicly available IS datasets is considered as an obstacle that prevents the extensive use of deep learning models in IS extraction. Therefore, this study first creates an open and accurate IS dataset based on optical and SAR images, and then proposes a semantic segmentation network based on cross fusion of optical and SAR images features, namely CroFuseNet, for IS extraction. In CroFuseNet, we design a cross fusion module (CFM) to fuse features of optical and SAR images to achieve better complementarity between the two types of images. And we propose a multimodal features aggregation (MFA) module to aggregate specific high-level features from optical and SAR images. To validate the proposed CroFuseNet, we compare it with two classical machine learning algorithms and four state-of-the-art deep learning models. The proposed model has the highest accuracy, with OA, MIoU and F1-Score of 97.77%, 0.9495 and 0.9770 respectively. The quantitative and qualitative experimental results demonstrate that the proposed model is superior to these comparative algorithms.

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.

Urban impervious surface (UIS) is an essential indicator to measure urban ecology, and understanding its temporal and spatial distribution is of great significance for alleviating urban diseases such as heat island. Although remote sensing techniques have been widely used to extract UIS, the capability of synthetic aperture radar (SAR) technology in UIS extraction has not been thoroughly investigated. Accordingly, this study systematically analyzed the role of the backscattering and interferometric coherence features of dual polarized (VH/VV) SAR in UIS extraction, taking Tianjin city, China, as a study case. Specifically, we used seasonal time series Sentinel-1B backscattering and coherence images to extract UIS via two object-based classifiers. We further investigated the seasonal, polarization, and precipitation effects on UIS extraction using SAR images. Our findings suggested that, for coherence images, the extraction accuracy of UIS in summer is the highest, while for backscattering, that in spring and winter are the highest. Our results also suggested that VH polarization is more suitable for UIS extraction compared with VV polarization. These findings of this study confirmed the utility and effectiveness of time series SAR backscattering and coherence images in UIS extraction, providing an essential reference for UIS extraction in other places with a similar climate. We encourage more efforts to be made towards UIS extraction using SAR images and towards designing a filling scheme for the UIS extraction in a continuous manner, especially in cloudy and rainy areas.