X-band mini SAR radar on eight-rotor mini-UAV

Yan, Jiali; Guo, Ji; Lu, Qianrong; Wang, Kaizhi; Liu, Xingzhao

doi:10.1109/igarss.2016.7730750

Cited by 12 publications

(11 citation statements)

References 3 publications

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“…Low-frequency ultrawideband (UWB) millimeter radar has good penetration capacity and offers potential for imaging building interiors. Previous demonstrations of UAV SAR include the P, L, and C bands [212] for civil and deformation monitoring applications; X band [213] for calculating the scattering characteristics of complex targets; P and C bands for estimating the terrain height of a eucalyptus forest [214]; UWB [215] for snow coverage scanning; and W band [216] to minimize the size and weight of the hardware, among others, and different or full polarization combinations, e.g., horizontal and vertical backscatters.…”

Section: Sensors and Platformsmentioning

confidence: 99%

Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions

Liang

Kukko

Balenović

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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Section: Sensors and Platformsmentioning

confidence: 99%

Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions

Liang

Kukko

Balenović

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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“…The use of unmanned aerial vehicles (UAVs) is one way to keep costs down, while also making it possible to venture into hazardous environments [2]. Small consumer UAVs (drones) are becoming less expensive, and have been used for SAR in several previous studies [3][4][5][6][7][8][9][10][11][12]. Furthermore, a group of drones can be less vulnerable than a single-UAV system, and can even be used for three-dimensional imaging by combining radar data from individual drones using beamforming techniques [13].…”

Section: Introductionmentioning

confidence: 99%

Small UAV-based SAR system using low-cost radar, position, and attitude sensors with onboard imaging capability

Svedin

Bernland

Gustafsson

et al. 2021

Int. J. Microw. Wireless Technol.

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This paper describes a small unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) system using low-cost radar (5–6 GHz), position (GNSS/RTK) and attitude (IMU) sensors for the generation of high-resolution images. Measurements using straight as well as highly curved flight trajectories and varying flight speeds are presented, showing range and cross-range lobe-widths close to the theoretical limits. An analysis of the improvements obtained by the use of attitude angles (roll, pitch, and yaw), to correct for the relative offsets in antenna positions as the UAV moves, is included. A capability to generate SAR images onboard with the back-projection algorithm has been implemented using a GPU accelerated single-board computer. Generated images are transmitted to ground using a Wi-Fi data link.

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“…As shown in Figure 2, SAR carriers flying at different altitudes above the same location point result in different shadow areas. The flight altitude of airborne SAR ranges from 3,000 m to 12,000 m. Recently, some miniature unmanned aerial vehicles (UAVs) with SAR sensors can fly at an altitude of 500 m to perform mapping tasks on targets on the ground [8][9][10][11][12]. The shadows in the airborne SAR images are more visible than those in the spatial SAR images.…”

Section: Introductionmentioning

confidence: 99%

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Zhu¹

2021

IEEE Access

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The dark area in the SAR image corresponds to the zero-energy electromagnetic echo region because specular reflection occurs when the emitted electromagnetic wave (EMW) comes into contact with a smooth object on the ground. In addition, EWM sometimes cannot measure the entire portion of the ground object due to the incident angle of the actual aperture radar sensor and the height of the object on the ground, which creates a dark area in the SAR image called a SAR shadow. Therefore, it is challenging to distinguish the dark area from shadows or some ground objects with smooth surfaces. To address this problem, this paper proposes a novel deep marginal learning algorithm (DMLA) to implement the dark zone classification of airborne SAR images. Specifically, a novel Snake Search Algorithm (SSA) with a chain code is deployed to generate margin slices of dark areas in SAR images, which is used as the input to the following classification network. In addition, a deep convolutional neural network with simple architecture is constructed to train the margin slices of the SSA, which outputs the final classification results with labels. Finally, experiments on three public airborne SAR image datasets output the average classification accuracy of 98.775% for the dark margin slices and an accuracy of 100% to identify dark regions in the original SAR images. The proposed method not only contributes to a high classification accuracy but also improves time efficiency.

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X-band mini SAR radar on eight-rotor mini-UAV

Cited by 12 publications

References 3 publications

Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions

Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions

Small UAV-based SAR system using low-cost radar, position, and attitude sensors with onboard imaging capability

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

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