Synthetic aperture radar (SAR) is a kind of high-resolution imaging radar, which can work in any weather conditions and is often used for target positioning. Airborne SAR has the advantages of flexible scanning geometry and high spatial resolution compared with Spaceborne SAR, but the quality of SAR image and positioning accuracy is affected by air turbulence and mechanical vibration. In practical applications, it is vital to improve Airborne SAR’s geometric positioning accuracy without ground control points or other reference image information. When positioning a single SAR image with long-range, the Earth surface model is often used as an additional equation to solve the Range-Doppler (RD) model. However, the Earth surface equation is difficult to solve the target height accurately. This study proposed an Airborne SAR target positioning methodology based on a local linear approximation of the Earth surface equation. The proposed local Linear RD (L-RD) model establishes a linear constraint of the local elevation, and the iterative algorithm converges rapidly. To further improve the efficiency of geolocation, we propose a method to directly obtain the initial geolocation used for the iteration based on the geometric relationship. An iterative framework is also proposed to enhance positioning accuracy when DEM is available. The experimental results show that the proposed method can significantly improve the positioning accuracy and reliability of Airborne SAR positioning without control points compared to other methods.

Arbitrary-oriented vehicle detection via aerial imagery is essential in remote sensing and computer vision, with various applications in traffic management, disaster monitoring, smart cities, etc. In the last decade, we have seen notable progress in object detection in natural imagery; however, such development has been sluggish for airborne imagery, not only due to large-scale variations and various spins/appearances of instances but also due to the scarcity of the high-quality aerial datasets, which could reflect the complexities and challenges of real-world scenarios. To address this and to improve object detection research in remote sensing, we collected high-resolution images using different drone platforms spanning a large geographic area and introduced a multi-view dataset for vehicle detection in complex scenarios using aerial images (VSAI), featuring arbitrary-oriented views in aerial imagery, consisting of different types of complex real-world scenes. The imagery in our dataset was captured with a wide variety of camera angles, flight heights, times, weather conditions, and illuminations. VSAI contained 49,712 vehicle instances annotated with oriented bounding boxes and arbitrary quadrilateral bounding boxes (47,519 small vehicles and 2193 large vehicles); we also annotated the occlusion rate of the objects to further increase the generalization abilities of object detection networks. We conducted experiments to verify several state-of-the-art algorithms in vehicle detection on VSAI to form a baseline. As per our results, the VSAI dataset largely shows the complexity of the real world and poses significant challenges to existing object detection algorithms. The dataset is publicly available.

Automatic registration of optical and synthetic aperture radar (SAR) remote sensing images has been extensively researched for decades. However, fast and robust multimodal image registration is still a challenge due to the significant region differences in many remote sensing scenarios (e.g. city and sea region coexist in single harbor imaging). To address this problem, this paper proposes a novel optical-to-SAR registration method called FED-HOPC, which integrates the fast Fourier transform (FFT) and weighted edge density (WED) map into the histogram of oriented phase congruency (HOPC) based registration framework. FFT is used to accelerate the 3D normalized cross-correlation (NCC) for the coarse matching. WED map is employed to detect adaptive block-based interest points, which are used to build region aware HOPC descriptors for the following fine registration. To evaluate the performance of FED-HOPC, extensive experiments on four optical-to-SAR registration datasets are conducted. The results demonstrate that FED-HOPC effectively register optical-to-SAR images with significant region differences and outperforms several state-of-the-art methods, including HOPC, MIND, MIRD, and CFOG.

With the advancement of remote sensing technologies, multimodal image matching has become increasingly important. Multimodal remote sensing images exhibit significant differences in radiometric and geometric properties due to their distinct imaging principles and being contaminated by various types of noise. Therefore, multimodal image matching remains a challenging problem. This paper proposes a novel descriptor called Shape-Adaptive Modality Independent Region Descriptor (SA-MIRD) to achieve robust matching across modalities by leveraging local structural self-similarities within the images. The proposed method first models the noise of each modality and uses a Local Polynomial Approximation-Intersection of Confidence Interval (LPA-ICI) to fit the local texture patterns. It then constructs the descriptor based on regional gradients extracted from local structural masks. Experimental results demonstrate the effectiveness of our approach in extracting structural features from multimodal images with good noise robustness, especially in regions with limited texture, such as mountainous and island areas. The proposed descriptor achieves high accuracy and robustness in multimodal image matching.