A Benchmark Sentinel-1 SAR Dataset for Airport Detection

Wang, Daochang; Zhang, Fan; Ma, Fei; Hu, Wei; Tang, Yu; Zhou, Yongsheng

doi:10.1109/jstars.2022.3192063

Cited by 3 publications

(1 citation statement)

References 64 publications

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“…As mentioned in the references, a precise runway detection method based on YOLOv5 is proposed in [26], a benchmark for airport detection using Sentinel-1 SAR (Synthetic Aperture Radar) is introduced in [66], and several detection approaches based on deep learning principles are presented in [14,[67][68][69]. Presently, only private aviation companies are endeavoring to research deep learning solutions of runway detection by forward-facing cameras located on the aircraft's nose or wings, which has achieved notable success in vision-based autonomous landing systems such as the capabilities of the autonomous taxiing, taking off, and landing of Airbus [70] and the Daedalean project [71].…”

Section: Vision-based Runway Segmentationmentioning

confidence: 99%

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

Wang,

Feng,

Zhao

et al. 2024

Remote Sensing

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Visual navigation, characterized by its autonomous capabilities, cost effectiveness, and robust resistance to interference, serves as the foundation for vision-based autonomous landing systems. These systems rely heavily on runway instance segmentation, which accurately divides runway areas and provides precise information for unmanned aerial vehicle (UAV) navigation. However, current research primarily focuses on runway detection but lacks relevant runway instance segmentation datasets. To address this research gap, we created the Runway Landing Dataset (RLD), a benchmark dataset that focuses on runway instance segmentation mainly based on X-Plane. To overcome the challenges of large-scale changes and input image angle differences in runway instance segmentation tasks, we propose a vision-based autonomous landing segmentation network (VALNet) that uses band-pass filters, where a Context Enhancement Module (CEM) guides the model to learn adaptive “band” information through heatmaps, while an Orientation Adaptation Module (OAM) of a triple-channel architecture to fully utilize rotation information enhances the model’s ability to capture input image rotation transformations. Extensive experiments on RLD demonstrate that the new method has significantly improved performance. The visualization results further confirm the effectiveness and interpretability of VALNet in the face of large-scale changes and angle differences. This research not only advances the development of runway instance segmentation but also highlights the potential application value of VALNet in vision-based autonomous landing systems. Additionally, RLD is publicly available.

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Section: Vision-based Runway Segmentationmentioning

confidence: 99%

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

Wang,

Feng,

Zhao

et al. 2024

Remote Sensing

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BARS: a benchmark for airport runway segmentation

Chen

Zhang

et al. 2023

Appl Intell

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SAR Image Registration: The Combination of Nonlinear Diffusion Filtering, Hessian Features and Edge Points

Tang,

Wei,

Zhuang

2024

Sensors

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Synthetic aperture radar (SAR) image registration is an important process in many applications, such as image stitching and remote sensing surveillance. The registration accuracy is commonly affected by the presence of speckle noise in SAR images. When speckle noise is intense, the number of image features acquired by single-feature-based methods is insufficient. An SAR image registration method that combines nonlinear diffusion filtering, Hessian features and edge points is proposed in this paper to reduce speckle noise and obtain more image features. The proposed method uses the infinite symmetric exponential filter (ISEF) for image pre-processing and nonlinear diffusion filtering for scale-space construction. These measures can remove speckle noise from SAR images while preserving image edges. Hessian features and edge points are also employed as image features to optimize the utilization of feature information. Experiments with different noise levels, geometric transformations and image scenes demonstrate that the proposed method effectively improves the accuracy of SAR image registration compared with the SIFT-OCT, SAR-SIFT, Harris-SIFT, NF-Hessian and KAZE-SAR algorithms.

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A Benchmark Sentinel-1 SAR Dataset for Airport Detection

Cited by 3 publications

References 64 publications

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

VALNet: Vision-Based Autonomous Landing with Airport Runway Instance Segmentation

BARS: a benchmark for airport runway segmentation

SAR Image Registration: The Combination of Nonlinear Diffusion Filtering, Hessian Features and Edge Points

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