Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Extracting animal migration pattern from weather radar observation based on deep convolutional neural networks

The weather radar can operate in all weathers and all time, and has a large coverage area. Besides monitoring the weather, the weather radar can receive other echoes including biological echoes. In order to utilise weather radar biological monitoring capability, recognising and classifying local insect and bird echoes is one of the biggest obstacles for analysing their migration, foraging, and reproduction activities. Here, a pixel-wise classification method based on the fully convolutional network (FCN) is proposed which is trained by the radar reflectivity and the spectral width images. Moreover, to increase the biometric detection accuracy, the region growing method is combined for achieving the region edge alignment. Finally, the proposed method is validated based on the real weather radar datasets in Yantai. The FCN training results have a high pixel accuracy of 92.96%, and the region growing method performs well in the edge alignment.

References

    1. 1)
      • 13. Pinheiro, P.H.O., Collobert, R.: ‘Recurrent convolutional neural networks for scene labeling’. Int. Conf. on Machine Learning, Beijing, China, June 2014, pp. 7382.
    2. 2)
      • 8. Dokter, A.M., Liechti, F., Stark, H., et al: ‘Bird migration flight altitudes studied by a network of operational weather radars’, J. R. Soc. Interface, 2011, 8, (54), pp. 3043.
    3. 3)
      • 1. Chilson, P.B., Frick, W.F., Kelly, J.F., et al: ‘Partly cloudy with a chance of migration: weather, radars, and aeroecology’, Bull. Am. Meteorol. Soc., 2012, 93, (5), pp. 669686.
    4. 4)
      • 3. Zrnic, D.S., Ryzhkov, A.V.: ‘Observations of insects and birds with a polarimetric radar’, IEEE Trans. Geosci. Remote Sens., 1998, 36, (2), pp. 661668.
    5. 5)
      • 14. Hariharan, B., Arbeláez, P., Girshick, R., et al: ‘Simultaneous detection and segmentation’, Lect. Notes Comput. Sci., 2014, 8695, pp. 297312.
    6. 6)
      • 6. Dufton, D.R.L., Collier, C.G.: ‘Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments’, Atmos. Meas. Tech., 2015, 8, (10), pp. 50255063.
    7. 7)
      • 9. Long, J., Shelhamer, E., Darrell, T.: ‘Fully convolutional networks for semantic segmentation’. Computer Vision Pattern Recognition, Boston, USA, June 2015, pp. 34313440.
    8. 8)
      • 18. Sun, T., Neuvo, Y.: ‘Detail-preserving median based filters in image processing’, Pattern Recognit. Lett., 1994, 15, (4), pp. 341347.
    9. 9)
      • 4. Koistinen, J.: ‘Bird migration patterns on weather radars’, Phys. Chem. Earth B Hydrol. Oceans Atmos., 2000, 25, (10), pp. 11851193.
    10. 10)
      • 11. Dan, C.C., Giusti, A., Gambardella, L.M., et al: ‘Deep neural networks segment neuronal membranes in electron microscopy images’, Adv. Neural Inf. Proc. Syst., 2012, 25, pp. 28522860.
    11. 11)
      • 5. Kessinger, C., Ellis, S., Andel, JV.: ‘P1. 6 The radar echo classifier: a fuzzy logic algorithm for the WSR-88D’. 3rd Conf. Artificial Intelligence Applications Environmental Science, California, USA, February 2003, pp. 111.
    12. 12)
      • 17. Haralick, R.M., Sternberg, S.R., Zhuang, X.: ‘Image analysis using mathematical morphology’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 9, (4), pp. 532550.
    13. 13)
      • 2. Kelly, J.F., Horton, K.G.: ‘Toward a predictive macrosystems framework for migration ecology’, Glob. Ecol. Biogeography, 2016, 25, (10), pp. 11591165.
    14. 14)
      • 12. Farabet, C., Couprie, C., Najman, L., et al: ‘Learning hierarchical features for scene labeling’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (8), pp. 19151929.
    15. 15)
      • 10. Ning, F., Delhomme, D., Lecun, Y., et al: ‘Toward automatic phenotyping of developing embryos from videos’, IEEE Trans. Image Process., 2005, 14, (9), pp. 13601371.
    16. 16)
      • 15. Bischof: ‘Seeded region growing’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 16, (6), pp. 641647.
    17. 17)
      • 16. Fan, J., Yau, D.Y., Elmagarmid, A.K., et al: ‘Automatic image segmentation by integrating color-edge extraction and seeded region growing’, IEEE Trans. Image Process., 2001, 10, (10), pp. 14541466.
    18. 18)
      • 7. Chowdhury, A.R., Sheldon, D., Maji, S., et al: ‘Distinguishing weather phenomena from bird migration patterns in radar imagery’. Computer Vision Pattern Recognition Workshops, Las Vegas, USA, June 2016, pp. 276283.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2019.0041
Loading

Related content

content/journals/10.1049/joe.2019.0041
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address