It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology.

Synthetic aperture radar (SAR) image is an effective remote sensing data source for geographic surveys. However, accurate land cover mapping based on SAR image in areas of complex terrain has become a challenge due to serious geometric distortions and the inadequate separation ability of dual-polarization data. To address these issues, a new land cover mapping framework which is suitable for complex terrain is proposed based on Gaofen-3 data of ascending and descending orbits. Firstly, the geometric distortion area is determined according to the local incident angle, based on analysis of the SAR imaging mechanism, and the correct polarization information of the opposite track is used to compensate for the geometric distortion area, including layovers and shadows. Then, the dual orbital polarization characteristics (DOPC) and dual polarization radar vegetation index (DpRVI) of dual-pol SAR data are extracted, and the optimal feature combination is found by means of Jeffries–Matusita (J-M) distance analysis. Finally, the deep learning method 2D convolutional neural network (2D-CNN) is applied to classify the compensated images. The proposed method was applied to a mountainous region of the Danjiangkou ecological protection area in China. The accuracy and reliability of the method were experimentally compared using the uncompensated images and the images without DpRVI. Quantitative evaluation revealed that the proposed method achieved better performance in complex terrain areas, with an overall accuracy (OA) score of 0.93, and a Kappa coefficient score of 0.92. Compared with the uncompensated image, OA increased by 5% and Kappa increased by 6%. Compared with the images without DpRVI, OA increased by 4% and Kappa increased by 5%. In summary, the results demonstrate the importance of ascending and descending orbit data to compensate geometric distortion and reveal the effectiveness of optimal feature combination including DpRVI. Its simple and effective polarization information compensation capability can broaden the promising application prospects of SAR images.