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.

Synthetic aperture radar (SAR) ship detection plays an important role in the field of maritime security. However, certain unique imaging properties make it challenging to extract the shape features of ships, such as speckle noise and strong scattering interference from irrelevant objects. These factors result in inaccurate ship localization and obvious false alarms under complex large-scale inshore scenes. To address this issue, we propose the scattering region topology network (SRT-Net), which can dynamically capture the comprehensive global context and enhance the ship saliency. This is achieved through two key modules, namely the scattering region topological structure pyramid (SRTP) and the ship saliency enhancement (SSE) module. The former provides richer semantic information to distinguish the object from the background, while the latter offers an extra pixel-level classification task to guide accurate bounding box regression. Thanks to the guidance of richer information, the proposed method can achieve fewer false alarms and enhance location accuracy. Additionally, we introduce a scale feature adaptive (SFA) loss to balance the attention to ships with various scales, which improves the robustness of multiscale ship detection. The proposed method achieves state-of-the-art performance under complex inshore scenes, and its effectiveness is verified by experiments on a large-scale SAR ship detection dataset (LSSDD) and a public SAR ship detection dataset (SSDD+).

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.

Intelligent ship detection algorithms for synthetic aperture radar (SAR) images have achieved significant results in Earth observation applications. By learning features such as scale, shape and texture from samples, they can quickly locate and recognize ships in complex backgrounds. However, due to the lack of use of polarization features, the upper bound of detection performance is still limited, especially under poor image quality conditions such as ambiguous interference. To solve this, the dual-polarization image feature fusion network (DPFF-Net) is proposed. The key of it lies in adaptive mining, enhancement and fusion of polarization features through the designed siamese structure, polarization-aware enhancement block (PAEB) and dynamic gated fusion block (DGFB). With fully utilizing complementary information hidden between co-polarization and cross-polarization data, more comprehensive and accurate features are obtained and used as the detect head input. Thus, the proposed algorithm achieves state-of-the-art performance, and its effectiveness are validated by experiments on dual-polarization SAR datasets.

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.

Due to variable imaging conditions, the resolution degradation often occurs in aerial images, which in turn impairs the performance upper bound of semantic segmentation. To solve this problem, super-resolution is placed before semantic segmentation as a pre-processing sub-task in most existing methods. The above two sub-tasks often form a unidirectional open-loop structure for relatively independent optimization, which constrains the ultimate segmentation performance improvement. To break down information barriers among them and form a more compact overall optimization, we propose an effective learning method named as Pixel-Wise Mutual-Feed Learning (PW-MFL) for segmenting images with resolution degradation. The key is to build auxiliary bidirectional connections, which contribute to the mutual pixel-wise spatial and feature information guidance during training. The feed-forward connection is realized by the Self-Attention Context Correlation (SACC) module, which enhances the intra-class semantic features of pixel positions with poor super-resolution performance by the fusion of that with superior performance. The feed-back connection is achieved by the Semantic Weighted Mapping (SWM) module, which aims to activate and adjust the detailed features of super-resolution in incorrectly segmented pixel positions via the semantic feature information. In addition, the Pixel-Aware Optimization (PAO) strategy is proposed to give more attention to optimizing specific pixel positions based on spatial information. Extensive experiments are conducted on three representative remote sensing segmentation benchmarks, ISPRS Vaihingen, ISPRS Potsdam, and iSAID datasets. The state-of-the-art segmentation level in resolution-degraded aerial images is achieved through the proposed learning method.

The terrain segmentation in polarimetric synthetic aperture radar (PolSAR) images is an important task for image interpretation. Since the speckle noise and complex scattering mechanism exist in SAR images, the classification results achieved by traditional methods appear fragmented. Gradually, deep-learning-based methods are proposed to solve this problem. However, only the amplitude data in the SAR image is utilized, which limits the classification precision. In this letter, a novel method based on a dual-attention fusion network (DAFN) is presented. DAFN is mainly composed of a two-way structure encoder for feature extraction and the attention-based fusion module. Considering the terrain characteristic and the SAR imaging mechanism, the introduction of the polarization information in DAFN increases the discrimination of different categories, which contributes to the consistent and accurate fine-grained classification results. To demonstrate the effectiveness of the proposed method, the corresponding experiments are done based on a GaoFen-3 satellite full-polarization SAR data set, in which the superior performance in terrain segmentation is obtained.

Boundary information is essential for the semantic segmentation of remote sensing images. However, most existing methods were designed to establish strong contextual information while losing detailed information, making it challenging to extract and recover boundaries accurately. In this paper, a boundary-sensitive network (BSNet) is proposed to address this problem via dynamic hybrid gradient convolution (DHGC) and coordinate sensitive attention (CSA). Specifically, in the feature extraction stage, we propose dynamic hybrid gradient convolution (DHGC) to replace vanilla convolution, which adaptively aggregates one vanilla convolution kernel and two gradient convolution kernels (GCKs) into a new operator to enhance boundary information extraction. The GCKs are proposed to explicitly encode boundary information, which are inspired by traditional Sobel operators. In the feature recovery stage, the coordinate sensitive attention (CSA) is introduced. This module is used to reconstruct the sharp and detailed segmentation results by adaptively modeling the boundary information and long-range dependencies in the low-level features as the assistance of high-level features. Note that DHGC and CSA are plug-and-play modules. We evaluate the proposed BSNet on three public data sets: the ISPRS 2-D semantic labeling Vaihingen, Potsdam benchmark and iSAID data set. The experimental results indicate that BSNet is a highly effective architecture that produces sharper predictions around object boundaries and significantly improves the segmentation accuracy. Our method demonstrates superior performance on the Vaihingen, Potsdam benchmark and iSAID data set, in terms of the mean F1, with improvements of 4.6%, 2.3% and 2.4% over strong baselines, respectively. The code and models will be made publicly available.

Range sidelobe is very common in synthetic aperture radar (SAR) images, particularly when imaging scene includes strongly scattering targets such as ships or complex buildings. As a kind of interference, it may reduce the image quality and hinder the image interpretation. Hence, range sidelobe suppression is an important mission for SAR images. The main task of mitigating the sidelobe is how to achieve the most effective suppression with the minimal resolution loss and signal-to-noise ratio (SNR) loss. However, the widely recognized classic method, spatially variant apodization (SVA), still has a lot of residual sidelobe energy and other problems. This article proposes a novel suppression approach based on time-variant transmission of chaotic frequency modulation (CFM) signals. The key is to build an appropriate transmitted signal set, where the signals are generated by various chaotic initial states and the same special map with low mixing rate and uniform invariant probability density (IPD). Due to their beneficial autocorrelation properties, the proposed approach achieves superior performance in range sidelobe suppression and resolution preservation. More importantly, it maintains the energy of the signals and overcomes the SNR loss that occurs in some classic methods, such as spectral weighting (SW) and SVA. In addition, it is suitable for both vertical and squint side-looking mode and can well reconstruct the weakly scattering targets which are severely disturbed by range sidelobe. All of them are validated by comparative experiments.

The resolution degradation poses a huge challenge for object detection (OD) in the aerial imagery. Existing methods utilize super resolution (SR) based on Generative Adversarial Network (GAN) to restore texture details in degraded images. However, constrained detection results are still acquired due to the object feature difference between restored and clear images. Therefore, we propose a simple-yet-effective learning method called Mutual-Feed Learning (MFL) to solve the problem in this paper. A closed-loop structure is designed via building the feedback connection based on the feedforward connection between the two tasks. It effectively delivers the object spatial and feature information from OD to SR, and provides restoration-enhanced images from SR to OD. Specifically, a Feedback of Region of Interest (FROI) module is introduced to realize a region-level discrimination under the guidance of object information. It guides the discrimination process of super resolution. Furthermore, a Multi-Scale Object Information (MSOI) module is developed to implement a feature-level restoration by narrowing differences in object-related features. It improves the generation process of super resolution. Then object detection can be performed in restoration-enhanced images to obtain more accurate results. Extensive experiments over NWPU VHR-10, COWC, and FAIR1M dataset show that the method can achieve state-of-the-art results.

Lightweight object detector is currently gaining more and more popularity in remote sensing. In general, it’s hard for lightweight detectors to achieve competitive performance compared to traditional deep models, while knowledge distillation is a promising training method to tackle the issue. Since the background is more complicated and the object size varies extremely in remote sensing images, it will deliver lots of noise and affect the training performance when directly applying the existing knowledge distillation methods. To tackle the above problems, we propose an Adaptive Reinforcement Supervision Distillation (ARSD) framework to promote the detection capability of the lightweight model. Firstly, we put forward a multiscale core features imitation (MCFI) module for transferring the knowledge of features, which can adaptively select the multiscale core features of objects for distillation and focus more on the features of small objects by an area-weighted strategy. In addition, a strict supervision regression distillation (SSRD) module is designed to select the optimal regression results for distillation, which facilitates the student to effectively imitate the more precise regression output of the teacher network. Massive experiments on the DOTA, DIOR, and NWPU VHR-10 datasets prove that ARSD outperforms the existing distillation SOTA methods. Moreover, the performance of lightweight model trained with our method transcends other classic heavy and lightweight detectors, which beneficiates the development of lightweight models.

Azimuth ambiguity may introduce false targets into synthetic aperture radar images, particularly likely in inshore and oceanic observation. To suppress the azimuth ambiguities for any acquisition mode, a new model is developed to describe the impact of spatially variant azimuth antenna pattern weighting on azimuth ambiguities. By accurately estimating the ratio of ambiguous to main zone energy based on the model, the proposed algorithm selects the subspectra with less ambiguous disturbance, and adopts extrapolation with weighted energy measure to obtain a full spectrum. Due to spectral selection and extrapolation, the novel algorithm achieves superior performance in azimuth ambiguity suppression and resolution preservation, which is compared with the classical algorithm, and validated by applying TerraSAR-X and RADARSAT-2 images.

An innovative method applied for suppression sidelobe in synthetic aperture radar (SAR) images is presented in this paper. The basic idea is to decompose the chirp signal into the unrelated chaos frequency modulation (CFM) signals uniformly in CFM space and add up their pulse compression results. As various CFM signal has different ideal thumbtack autocorrelation function, the energy from the sidelobe of the above-mentioned adding result is kept in a low level when compared with that from mainlobe. Our method can suppress the sidelobe to -40dB and hardly deteriorate resolution simultaneously. The corresponding performances are verified in the simulation experiments.

An innovative algorithm to suppress the strong azimuth ambiguity in single-look complex (SLC) synthetic aperture radar (SAR) images is presented. The basic idea is to construct a subspace with low ambiguous power and project the original image to the aforementioned subspace to suppress the azimuth ambiguity by the minimum mean square error estimation (MMSE). Compared with most traditional approaches, the proposed one is suitable for any distributed scene and any acquisition mode. Moreover, the proposed approach seems to keep the resolution in a reasonable level and not rely on the system parameters extremely. Raw data from the TerraSAR-X have been used to validate the effect of the azimuth ambiguity suppression by using the new approach.