The GaoFen-3 (GF-3) satellite is the first fully polarimetric synthetic aperture radar (SAR) satellite designed for civil use in China. The satellite operates in the C-band and has 12 imaging modes for various applications. Three fully polarimetric SAR (PolSAR) imaging modes are provided with a resolution of up to 8 m. Although polarimetric calibration (PolCAL) of the SAR system is periodically undertaken, there is still some residual distortion in the images. In order to assess the polarimetric accuracy of this satellite and improve the image quality, we analyzed the polarimetric distortion errors and performed a PolCAL experiment based on scattering properties and corner reflectors. The experiment indicates that the GF-3 images can meet the satellite’s polarimetric accuracy requirements, i.e., a channel imbalance of 0.5 dB in amplitude and ±10 degrees in phase and a crosstalk accuracy of −35 dB. However, some images still contain residual polarimetric distortion. The experiment also shows that the residual errors of the GF-3 standard images can be diminished after further PolCAL, with a channel imbalance of 0.26 dB in amplitude and ±0.2 degrees in phase and a crosstalk accuracy of −42 dB.

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.

In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China's newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products.

It is well known that polarimetric synthetic aperture radar (PolSAR) backscattering features are highly influenced by the variation of incidence angle (VIA), which usually hampers the classification of most grazing-angle-sensitive targets, such as land and ocean targets. To relieve this issue, various feature extraction approaches have been suggested to enhance the class discriminability while reducing the observed feature dimensionality. The Laplacian eigenmap-based dimension reduction (DR) has been proven to be an effective way to deal with VIA problems, provided that the manifold parameters [e.g., the heat kernel (HK)] have been optimally sought, which is often difficult in practice. In this letter, an adaptive Laplacian eigenmap-based DR method is presented to find a learned subspace where the local geometry with discriminative prior knowledge is preserved as much as possible while near optimal HK and scale factor parameters are automatically identified. The learned feature representation is then employed for the subsequent classification. The improved Laplacian eigenmap algorithm was validated by three uninhabited-aerial-vehicle-synthetic-aperture-radar L-band PolSAR images from the Gulf Deepwater Horizon oil spill, which were clearly impacted by the VIA phenomenon. The experimental results showed that the proposed algorithm works well in ocean target discrimination compared with the current common methods.