Aircraft detection in synthetic aperture radar (SAR) imagery is significant due to its critical role in various applications, including surveillance, reconnaissance, and security. However, given the background interference and discreteness of aircraft scattering, detectors are prone to acquire unremarkable aircraft features. These factors lead to false alarms and present difficulties in locating aircraft accurately. This article proposes an innovative scattering topology cue-based network (STC-Net), which enhances aircraft discriminability and more accurately evaluates the quality of the prediction results. We model the aircraft with the star topology (ST), which not only emphasizes critical components like the nose and wings but also explicitly links them as a cohesive unit. Based on the cue of ST, the ST space fusion module (ST-SFM) and the ST channel attention module (ST-CAM) are designed. The former integrates discrete components to reestablish the aircraft features based on neighboring information of ST, while the latter suppresses background interference to highlight the aircraft by exploiting node information of ST. In addition, completeness and consistency loss (CCLoss) function that includes the completeness-aware label and the positive sample weighting function is introduced. The completeness-aware label describe the localization accuracy by incorporating the degree of overlap of predicted results on ST, while the positive sample weighting function enhances the consistency of the classification and localization branches. Furthermore, experiments conducted on the Gaofen-3 SAR aircraft detection dataset (GF3ADD) and the publicly available SAR-AIRcraft-1.0 dataset demonstrate the effectiveness and generalizability of STC-Net, with our method achieving state-of-the-art performance.

Object detection and recognition (OD&R) based on deep learning is a hot topic in the application of synthetic aperture radar (SAR). These methodologies based on deep learning are inherently data-driven, which means that their performance is subjected to the corresponding datasets. Although existing datasets have included some common targets collected from real-valued intensity SAR images, there still exist some limitations in terms of quantity, categories, diversities, and data domain. Hence, it is urgent to establish a large-quantity benchmark for fine-grained OD&R on complex-valued SAR images, which contains rich signal-domain features well coupled with classical physical modeling. In addition, considering the unique imaging characteristics and diverse imaging conditions, some important attribute information, such as incidence and attitude angles, is necessary to be attached. In this article, we propose a novel benchmark dataset with more than 340k instances for fine-grained OD&R based on single-look complex (SLC) SAR images, which is named FAIR-CSAR. We collected complex-valued SAR images with a resolution of 1–5 m from 175 entire images of Gaofen-3 covering 32 cities and multiple sea areas worldwide. All instances in the FAIR-CSAR are annotated by oriented bounding boxes (OBBs), covering five major categories and 22 subcategories. Compared with existing datasets dedicated to OD&R, the FAIR-CSAR dataset has four particular advantages: 1) it contains complex-valued SAR images from various acquisition modes and polarization modes, including full-scale signal-domain features for object recognition; 2) it is much larger than other existing OD&R datasets in terms of quantity of instances; 3) it provides more fine-grained category annotation and more detailed attribute information; and 4) it provides more challenging images with some common imaging phenomena, such as speckle noise and azimuth ambiguities. To establish a baseline adapted for SLC SAR images, a multidomain feature extraction and fusion network (MDNet) is proposed as a novel framework to mine detailed information underlying various domains. A series of state-of-the-art (SOTA) algorithms are applied on the FAIR-CSAR to build the fine-grained OD&R benchmark. Experimental results indicate that FAIR-CSAR is closer to practical application and more challenging than existing datasets for SAR images.

Sidelobes commonly disturb synthetic aperture radar (SAR) image understanding and interpretation. Traditional spatially variant filtering algorithms achieve a superior tradeoff between sidelobe suppression and resolution preservation by means of adaptively calculating filtering parameters under some specific restrictions, such as filter design restriction and minimum amplitude constraint (MAC). These restriction aims to obtain an efficient analytical solution for filters, which is easy to calculate under unsupervised conditions. However, the restriction scope is so narrow that the suppression performance achieved by these filters is limited. Also, since the unsupervised optimization based on MAC indiscriminately minimizes amplitude, the main-lobe loss is unavoidable. To further improve the performance, a spatially variant convolution neural network (SVNN) is proposed, which consists of two core modules. One is the spatially variant filter generation (SVFG) module, adaptively generating superior spatially variant filters under more relaxed restrictions. The other is a paralleled shifted convolution (PSC) module, converting the signal format to achieve a fast and parallel spatially variant filtering process. Benefiting from more relaxed filter restrictions, the novel network successfully achieves better performance on sidelobe suppression. In addition, with supervised optimization based on another more accurate restriction, namely, minimum error constraint (MEC), the proposed algorithm also achieves superior main-lobe maintenance. All of them are validated by comparative experiments based on satellite data from GaoFen-3 and TerraSAR-X, and our proposed method achieves state-of-the-art performance. The entire project is available athttps://github.com/suoyuxi/SVNN.

Synthetic aperture radar (SAR) possesses significant advantages in aircraft detection due to its all-day and all-weather monitoring capability, but some unique problems in SAR images decrease the performance of aircraft detection. The speckle effect and excessive dynamic range are the most common problems that interfere with the visual features in SAR images and deteriorate detection performance. However, there lacks a detection-oriented image enhancement algorithm to collaboratively solve these two problems. An adaptive image enhancement algorithm is proposed to improve the performance of aircraft detection in SAR images. The proposed image enhancement algorithm provides a pseudocolor image through speckle suppression and channel combination, which consists of the speckle noise suppression channel, strong scattering feature enhancement channel, and weak scattering feature enhancement channel. The speckle noise suppression is achieved by a despeckle network, and the radiational feature enhancement channels are derived from an adaptive quantization method based on the characteristics of amplitude distribution. By optimizing the quality of the input image, the proposed image enhancement algorithm improves the performance of aircraft detection. Experiments based on datasets acquired by GaoFen-3 satellites indicate that the proposed algorithms significantly improve the detection performance of various types of detectors. The source project is available athttps://github.com/suoyuxi/ChannelEnhancement.

Aliasing artifacts introduced by azimuth ambiguity seriously impact the interpretation of synthetic aperture radar images. To achieve parameter-free and fast azimuth ambiguity suppression, a novel deep learning model is designed to estimate the ambiguous signal intensity to total signal intensity ratio in the range-Doppler domain. This model does not depend on processing parameters and can be applied in any acquisition mode. The mean shift algorithm is applied to select less ambiguous subspectra according to the estimation result. The selected subspectra are restored to a full spectrum with an energy concentrated extrapolation method to preserve the resolution. The enhanced spectral selection and extrapolation algorithm overcomes the dependence on processing parameters, and experiments based on TerraSAR-X and Radarsat-2 images indicate that the proposed algorithm suppresses the azimuth ambiguity significantly.