scholarly journals Sparsity‐based autoencoders for denoising cluttered radar signatures

2021 ◽  
Author(s):  
Shobha Sundar Ram ◽  
Shelly Vishwakarma ◽  
Akanksha Sneh ◽  
Kainat Yasmeen
Keyword(s):  
Radar Signatures

scholarly journals Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2075
Author(s):  
J. David Ballester-Berman ◽  
Maria Rastoll-Gimenez
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Oil Palm ◽  
Time Interval ◽  
Potential Candidate ◽  
Initial Growth ◽  
Natural Forests ◽  
Wet Season ◽  
Radar Signatures ◽  
The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

scholarly journals Radar signatures of snowflake riming: A modeling study

2015 ◽  
Vol 2 (8) ◽  
pp. 346-358 ◽  
Author(s):  
Jussi Leinonen ◽  
Wanda Szyrmer
Keyword(s):  
Radar Signatures ◽  
Modeling Study

scholarly journals Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

2017 ◽  
Vol 17 (19) ◽  
pp. 12011-12030 ◽  
Author(s):  
Mathias Gergely ◽  
Steven J. Cooper ◽  
Timothy J. Garrett
Keyword(s):  
Surface Area ◽  
Cross Sections ◽  
Volume Ratio ◽  
High Sensitivity ◽  
Maximum Size ◽  
Strong Impact ◽  
Triple Frequency ◽  
Bounding Volume ◽  
Wide Range ◽  
Radar Signatures

Abstract. The snowflake microstructure determines the microwave scattering properties of individual snowflakes and has a strong impact on snowfall radar signatures. In this study, individual snowflakes are represented by collections of randomly distributed ice spheres where the size and number of the constituent ice spheres are specified by the snowflake mass and surface-area-to-volume ratio (SAV) and the bounding volume of each ice sphere collection is given by the snowflake maximum dimension. Radar backscatter cross sections for the ice sphere collections are calculated at X-, Ku-, Ka-, and W-band frequencies and then used to model triple-frequency radar signatures for exponential snowflake size distributions (SSDs). Additionally, snowflake complexity values obtained from high-resolution multi-view snowflake images are used as an indicator of snowflake SAV to derive snowfall triple-frequency radar signatures. The modeled snowfall triple-frequency radar signatures cover a wide range of triple-frequency signatures that were previously determined from radar reflectivity measurements and illustrate characteristic differences related to snow type, quantified through snowflake SAV, and snowflake size. The results show high sensitivity to snowflake SAV and SSD maximum size but are generally less affected by uncertainties in the parameterization of snowflake mass, indicating the importance of snowflake SAV for the interpretation of snowfall triple-frequency radar signatures.

Simulation and analysis of polarimetric radar signatures of human gaits

2014 ◽  
Vol 50 (3) ◽  
pp. 2164-2175 ◽  
Author(s):  
James Park ◽  
Joel Johnson ◽  
Ninoslav Majurec ◽  
Mark Frankford ◽  
Kyle Stewart ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Radar Signatures
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