Detection of sea ice based on BeiDou-reflected signals

Gao, Hongxing; Yang, Dongkai; Li, Weiqiang; Wang, Qiang; Wang, Feng; Yin, Cong

doi:10.1109/igarss.2016.7730271

Cited by 4 publications

(2 citation statements)

References 12 publications

(10 reference statements)

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“…Han et al [19] proposed a method for sea ice image classification based on heterogeneous data fusion and deep learning, effectively fusing SAR and optical imagery to enhance the precision of remote sensing sea ice classification. Gao et al [20] evaluated accurate observations of remote sensing sea ice using reflected signals from the BeiDou Geostationary Earth Orbit (GEO) satellite. Gao et al [21] introduced a convolutional-wavelet neural network-based approach for sea ice change detection, mitigating the influence of inherent speckle noise in multi-temporal SAR images.…”

Section: Introductionmentioning

confidence: 99%

Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture

Li,

Wang,

et al. 2024

J. Phys.: Conf. Ser.

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Shipborne sea ice detection aboard icebreakers plays a paramount role in polar navigation. The continuous evolution of deep learning semantic segmentation networks has promoted the advancement of sea ice detection tasks. At this stage, there are relatively few studies on shipboard sea ice detection, and the accuracy of polar sea ice detection will be reduced due to problems such as blurred sea fog and indistinct boundaries. In this study, a shipboard sea ice detection dataset is constructed, and a sea ice detection method that combines multi-branch attention feature alignment and multi-scale feature extraction is proposed. The heterogeneous receptive field enhancement atrous spatial convolution pooling pyramid module is designed, and the feature alignment module based on the attention mechanism is constructed, which strengthens the model’s extraction of sea ice features and elevates representation performance. Experimental results underscore the heightened precision of our approach in sea ice detection, to some extent alleviating the issue of missed detections in new ice. It constitutes a positive contribution towards advancing shipborne sea ice detection in polar environments.

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Section: Introductionmentioning

confidence: 99%

Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture

Li,

Wang,

et al. 2024

J. Phys.: Conf. Ser.

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“…The results showed that the concentration of sea ice is correlated with the polarization ratio of BeiDou GEO satellites [24]. The same application was also tested in [25]. The soil moisture retrieval method was proposed based on the reflected signals from BeiDou GEO satellites, using a support vector regression machine (SVRM)-assisted method.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals

Gao

Yang

Hong

et al. 2019

Sensors

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This paper presents a method of ground target detection using reflected signals of BeiDou satellites. The phase difference information, which is the output of the phase-lock loop (PLL) in the tracking process, is an important observation in this technique. The geometric relationships between the specular point of different BeiDou satellites and the target are established. In addition, the detection and false alarm probability are also analyzed. In order to verify the reliability of the method, an experiment in the suburb area of Beijing was completed. The target was placed in the coverage area of the left-handed circular polarization (LHCP) antenna for two time periods (10–20 s and 40–55 s). By observing the phase difference in BeiDou reflected signals in the presence of a target, it was found that the changing trend was in good agreement with the target placement time periods. In the second experiment, the target moved east and west at a speed of 0.5 m/s, and the range of motion was 6 m. During the acquisition of the BeiDou reflection signal, the target passed through the antenna 14 times. The performance of target detection with different parameters was observed by extracting in-phase (I) branch component data, phase difference information, and the carrier-to-noise ratio (CNR) of five BeiDou reflected signals. The experimental results allowed three conclusions to be drawn as follows: (1) The target detection performance of the three parameters has a certain relationship with the altitude angle and the azimuth angle of the satellite; (2) target motion direction information can be reflected in the change of the satellite I branch component data; (3) The CNR information of different satellite reflected signals varies greatly when the target moves, which is quite different from that of the first experimental target when it is stationary. Thus, the feasibility of target detection using BeiDou reflection signal was demonstrated through these two experiments.

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Research advances in remote sensing monitoring of sea ice in the Bohai sea

Yuan

Liu

et al. 2021

Earth Sci Inform

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Detection of sea ice based on BeiDou-reflected signals

Cited by 4 publications

References 12 publications

Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture

Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture

Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals

Research advances in remote sensing monitoring of sea ice in the Bohai sea

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