Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement

Widespread availability of drones is associated with many new fascinating possibilities, which were reserved in the past for few. Unfortunately, this technology also has many negative consequences related to illegal activities (surveillance, smuggling). For this reason, particularly sensitive areas should be equipped with sensors capable of detecting the presence of even miniature drones from as far away as possible. A few techniques currently exist in this field; however, all have significant drawbacks. This study addresses a novel approach for small (<5 kg) drones detection technique based on a laser scanning and a method to discriminate UAVs from birds. The latter challenge is fundamental in minimizing the false alarm rate in each drone monitoring equipment. The paper describes the developed sensor and its performance in terms of drone vs. bird discrimination. The idea is based on simple cross-polarization ratio analysis of the optical echo received as a result of laser backscattering on the detected object. The obtained experimental results show that the proposed method does not always guarantee 100 percent discrimination efficiency, but provides certain confidence level distribution. Nevertheless, due to the hardware simplicity, this approach seems to be a valuable addition to the developed anti-drone laser scanner.

Oilseed Rape (Brassica napus L.) Phenology Estimation by Averaged Stokes-Related Parameters

Accurate and timely knowledge of crop phenology assists in planning and/or triggering appropriate farming activities. The multiple Polarimetric Synthetic Aperture Radar (PolSAR) technique shows great potential in crop phenology retrieval for its characterizations, such as short revisit time, all-weather monitoring and sensitivity to vegetation structure. This study aims to explore the potential of averaged Stokes-related parameters derived from multiple PolSAR data in oilseed rape phenology identification. In this study, the averaged Stokes-related parameters were first computed by two different wave polarimetric states. Then, the two groups of averaged Stokes-related parameters were generated and applied for analyzing averaged Stokes-related parameter sensitivity to oilseed rape phenology changes. At last, decision tree (DT) algorithms trained using 60% of the data were used for oilseed rape phenological stage classification. Four Stokes parameters (g0, g1, g2 and g3) and eight sub parameters (degree of polarization m, entropy H, ellipticity angle χ, orientation angle φ, degree of linear polarization Dolp, degree of circular polarization Docp, linear polarization ratio Lpr and circular polarization ratio Cpr) were extracted from a multi-temporal RADARSAT-2 dataset acquired during the whole oilseed rape growth cycle in 2013. Their sensitivities to oilseed rape phenology were analyzed versus five main rape phenology stages. In two groups (two different wave polarimetric states) of this study, g0, g1, g2, g3, m, H, Dolp and Lpr showed high sensitivity to oilseed rape growth stages while χ, φ, Docp and Cpr showed good performance for phenology classification in previous studies, which were quite noisy during the whole oilseed rape growth circle and showed unobvious sensitivity to the crop’s phenology change. The DT algorithms performed well in oilseed rape phenological stage identification. The results were verified at the parcel level with left 40% of the point dataset. Five phenology intervals of oilseed rape were identified with no more than three parameters by simple but robust decision tree algorithm groups. The identified phenology stages agree well with the ground measurements; the overall identification accuracies were 71.18% and 79.71%, respectively. For each growth stage, the best performance occurred at stage S1 with the accuracy of 95.65% for Group 1 and 94.23% for Group 2, and the worst performance occurred at stage S3 and S5 with the values around 60%. Most of the classification errors may resulted from the indistinguishability of S3 and S5 using Stokes-related parameters.

Sea Spike Suppression Method Based on Optimum Polarization Ratio in Airborne SAR Images

In the condition of ocean observation for high-resolution airborne synthetic aperture radar (SAR), sea spikes will cause serious interference to SAR image interpretation and marine target detection. In order to improve the ability of target detection, it is necessary to suppress sea spikes in SAR images. However, there is no report on sea spike suppression methods in SAR images. As a step forward, a sea spike suppression method based on optimum polarization ratio in airborne SAR images is proposed in this paper. This method is only applicable to the situation where VV and HH dual-polarized SAR data containing sea spikes are acquired at the same time. By calculating the optimum polarization ratio, this method further obtains the difference image of the panoramic area accomplishing sea spike suppression. This method is applied to a field airborne X-band SAR data, including ocean waves, oil spills and ships. The results show that the sea spikes are well suppressed, the contrast of ocean waves and the contrast of oil spills are improved, and the false alarm rate of ship detection is reduced. The discussions on these results demonstrate that the proposed method can effectively suppress sea spikes and improve the interpretability of SAR images.

Characteristics of Saline Soil in Extremely Arid Regions: A Case Study Using GF-3 and ALOS-2 Quad-Pol SAR Data in Qinghai, China

Soil salinization is a global problem, which seriously damages the ecological environment and considerably reduces agricultural productivity, especially in arid regions. Synthetic aperture radar (SAR) has been widely used in remote sensing due to its weather and sunlight independence. Polarimetric SAR has great potential for large-scale mapping and monitoring salt-affected soils. In this study, we investigate the characteristics of saline soil in extremely arid regions using dual-band quadrature-polarimetric (quad-pol) SAR images acquired by GF-3 (C-band) and ALOS-2 (L-band). Firstly, the effectiveness of the modified dielectric mixing model and integral equation model (IEM) in describing saline soil is evaluated. Secondly, the potential relationships between polarimetric parameters and salinity are discussed in both the C- and L-band, respectively, such as co-polarization ratio, scattering entropy H, and scattering angle α. Finally, a linear regression model for monitoring salt content is established. The main contributions of this article are as follows: (1) Simulation results suggest that the radar backscattering coefficient is a weak function of salinity at low water content, but our experimental data show that soil salinity significantly contributes to the radar backscattering coefficient, which indicates the modified dielectric mixing model and IEM model is not applicable in extremely arid areas. (2) A negative correlation between the co-polarization ratio and salinity is observed, and the correlation coefficients are 0.64 (C-band) and 0.71 (L-band). Besides, scattering entropy and scattering angle exhibit a positive correlation with salinity in the C-band with correlation coefficients 0.686 and 0.669, respectively, whereas a negative correlation is found in the L-band with correlation coefficients 0.682 and 0.680, respectively. This can be attributed to the different penetration depths and sensitivity to the surface roughness of the electromagnetic waves at two frequencies. (3) A regression model for salinity estimating based on radar backscattering coefficient, co-polarization ratio, and scattering entropy is established, with a determination coefficient (R2) of 0.79 and a root mean square error (RMSE) of 6.56%, allowing us to determine soil salinity from quad-pol SAR images without using backscattering models. Therefore, our results can be a reference for future soil salinity monitoring and inversion.

Geomagnetic anomaly associated with Fukushima earthquake on February 13th, 2021

In this study, we performed research on electromagnetic anomalies related to earthquakes as early signs (precursors) that occurred in Fukushima, Japan on February 13th, 2021. The research focused on the utilization of geomagnetic field data which was derived from the Kakioka (KAK), Kanoya (KNY), and Memambetsu (MMB) observatories, particularly in the ultra-low frequency (ULF) to detect earthquake precursors. The method of electromagnetic data processing was conducted by applying a polarization ratio. In addition, we improved the methodology by splitting the ULF data (which ranged from 0.01-0.1 Hz) into 9 central frequencies and picking up the highest value from each central frequency to get the polarization ratio. The anomaly of magnetic polarization was identified 2-3 weeks before the mainshock in a narrowband frequency in the range of 0.04-0.05 Hz.