To achieve efficient ship detection in high-resolution synthetic aperture radar images, an improved superpixel-level constant false alarm rate (CFAR) detection method is proposed with three modifications. First, the weighted information entropy (WIE) describes the statistical characteristics of superpixels, yielding a better distinction between target and clutter superpixels. Second, a two-stage CFAR detection scheme is proposed to detect target superpixels, including global detection and local detection. Specifically, the WIE-based global detector is utilized to prescreen candidate target superpixels (CTSs) and then the local CFAR detector is adaptively conducted over the selected CTSs to refine target superpixels. Third, the superpixels selected by the global detection can be excluded as outliers for clutter estimation, thus the disturbance from adjacent targets in multitarget situations can be reduced and more accurate detection results can be obtained. Compared with CFAR methods implemented with the sliding window technique, the computational burden of the proposed method is significantly reduced without loss of detection performance. Experiments of ship detection on three TerraSAR-X datasets are presented to validate the effectiveness of the proposed method.

In this letter, we propose a superpixel-level target detection approach for synthetic aperture radar (SAR) images. With superpixel segmentation, SAR image is divided into meaningful patches and more statistical information can be provided in superpixels compared with single pixels. The statistical difference between target and clutter superpixels can be measured with the intensity distributions of pixels in them. With the assumption of SAR data obeying Gamma distribution, the superpixel dissimilarity is defined. With this basis, the global and local contrast can be obtained and integrated to enhance target and suppress clutter simultaneously. Thus, better target detection performance can be achieved. Different from traditional target detection schemes based on backscattering difference between target and clutter pixels, the proposed method relies on the statistical difference of superpixels. The effectiveness of the proposed method can be demonstrated with experimental results on real SAR images.

This paper deals with the problem of fast focusing the spotlight synthetic aperture radar (SAR) data in high-resolution and high-squint mode, which has shown its potential capacity in various military SAR applications. Fast factorized backprojection (FFBP) is considered as an ideal methodology for highresolution and high-squint SAR imagery. However, FFBP performs pixel-by-pixel interpolation, yielding heavy computational burden and limiting its real-time processing. In this paper, target detection technique, which is used as a discrimination tool to retain the target pixels and reject the clutter and noise pixels, is integrated into the subaperture imaging chain during FFBP implementation, aiming to speed up the overall image formation time. Due to the characteristic of spotlight acquisition, an adaptive processing scheme is developed for multiple subaperture images detection. With the integrated target detection technique, only the target pixels need to be interpolated and accumulated coherently. Without loss of focusing performance, the number of interpolations is dramatically reduced. The proposed fast imaging algorithm is named as adaptive FFBP (AFFBP), and it has distinctive superiority for sparse scene reconstruction, such as the maritime target imaging. AFFBP has been successfully applied to two simulated scenes with high and low signal-to-noise ratios (SNRs), and two real measured datasets with squint angle high up to 50° and 70°, respectively. All these experiments demonstrate the effectiveness and efficiency improvement of the proposed AFFBP algorithm.

One open issue of target detection for synthetic aperture radar (SAR) images is the capture effect from the clutter edge and the interfering outliers, including surrounding targets in the multitarget environment, sidelobes, and ghosts. To address this issue, a superpixel-level constant false-alarm rate (CFAR) detector is proposed based on the truncated Gamma statistics for the multilook intensity SAR data. Superpixel segmentation serves as a preprocessing procedure to divide the SAR image into meaningful patches. By automatic clutter truncation in the superpixel-level background clutter window, the real clutter samples are preserved. The experimental results with real SAR images demonstrate that the proposed method achieves better goodness-of-fit performance for the real clutter background with outlier exclusion, yielding a higher target detection rate in the multitarget environments.

In RGB-infrared aerial image object detection, fully utilizing the advantages of both RGB and infrared images for effective detection is a key challenge in this field. In response to the challenges outlined, an RGB-infrared multi-branch progressive feature fusion method for object detection in aerial images is proposed. Specifically, since accurate detection of objects in aerial images usually requires extensive context information, considering the connection between objects and backgrounds, the global-local synergistic attention is constructed. To mitigate the impact of feature domain differences between RGB and infrared images during the complementary information filtering process, a multimodal complementary information filter for RGB and infrared images is designed on the basis of global-local synergistic attention. To fully exploit the advantages of the two types of model images, a multi-branch progressive feature fusion network is designed. The multi-branch progressive feature fusion network progressively fuses the enhanced features from the global-local synergistic attention with the filtered complementary information by the multimodal complementary information filter, resulting in a final fused feature that integrates the rich visual details of RGB images with the distinctive physical characteristics of infrared images. Results against publicly released datasets demonstrate that the proposed method achieves the state-of-the-art detection performance.

The constant false alarm rate (CFAR) detectors are well studied for ship detection in synthetic aperture radar (SAR) images, which suffer performance degradation due to the capture effect from interfering outliers, such as nearby targets, sidelobes, and ghosts in multitarget environments. To address this issue, the clutter truncation scheme is adopted to reduce the outlier contamination in clutter samples such that the accuracy of clutter modeling can be improved. However, the selection of clutter truncation depth is difficult, which often resorts to sensitivity study. In this article, the complex signal kurtosis (CSK) is first utilized as a statistical indicator for the decision of truncation depth to guarantee that the true clutter samples are maintained. Besides, a coarse-to-fine detection process is designed, including global superpixel proposal with the CSK and local identification of target pixels with the superpixel-level CFAR detector based on truncated statistics. During the local CFAR detection stage, the segmented superpixels provide convenient sample indexing for the iterative clutter truncation processing. The elevated performance achieved by the proposed method mainly benefits from the schemes of two-stage detection and automatic clutter truncation, yielding the increased detection efficiency and accuracy at the same time. Besides, false alarms caused by radio frequency interference can be reduced. In the experiment, the comparative results with state-of-the-art methods based on the Sentinel-1 and Gaofen-3 SAR data validate the performance of the proposed method.