Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well established for the three-dimensional (3D) reconstruction of urban buildings. Many methods have been proposed in the literature for TomoSAR inversion. These methods usually require fairly large data stacks (more than 20 images) for reliable reconstruction. Hence, they cannot be applied to the 3D reconstruction of urban buildings using Gaofen-3 (GF-3) data directly, because there are few images available on average in each city. This paper proposes a novel workflow for the 3D reconstruction of high-rise buildings using small data stacks. In this workflow, we combine the methods of contour line extraction (CLE) and reference-elevation multilooking relaxation (RM-RELAX) for the 3D reconstruction of high-rise buildings. The CLE method extracts contour lines of buildings relying on a data-driven approach and does not need to rely on any external data, which can provide prior knowledge for subsequent processing. The RM-RELAX method is an extension of RELAX, which can obtain more precise 3D inversion with multilooking and reject outliers with the reference elevation (which can be obtained by the prior knowledge of contour lines) of each pixel. The applicability of this workflow is demonstrated by using simulated data and real data with six SAR images acquired by the GF-3 satellite over an area in Beijing, China.

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain.

Very-Low Earth Orbit Synthetic Aperture Radar (VLEO SAR) satellites, defined as SAR satellites operating at orbital altitudes 350 km or below, offer distinct technical advantages compared to conventional SAR satellites. Equipped with a high-resolution SAR payload, the Haishao-1 (HS-1) satellite was successfully launched on 4 December 2024. According to publicly available information, the HS-1 satellite represents the world’s first VLEO SAR satellite and has successfully demonstrated 1-m resolution Stripmap mode imaging with continuous azimuth coverage. Through an analysis of the HS-1 satellite’s system parameters and imaging results, this paper comprehensively explores the advantages of VLEO SAR satellites over traditional orbit SAR satellites, particularly in terms of enhanced resolution, reduced payload costs, and improved constellation deployment capabilities. VLEO SAR satellites possess significant advantages, including the potential for higher-resolution imagery and lower-cost payload designs, positioning them for extensive application prospects in fields such as space-based military reconnaissance, natural resource surveying, and natural disaster monitoring.