access icon free Surface segmentation and environment change analysis using band ratio phenology index method – supervised aspect

Remote sensing is an escalating field that helps to monitor the earth in different perspectives like vegetation assessment, coastal studies, global warming analysis etc. Presently many satellites are orbiting the earth for taking multispectral imagery, which is working behind the principle remote sensing applications. Though there are mechanisms for image classification still innovative method is required to detect and monitor the physical characteristics of the environment. Weather forecasting, ecology assessment and irrigation management are relying upon the seasonal changes. This research study concentrates on seasonal change analysis by supervised image classification called Band Ratio Phenology Index (BRPI) method. This BRPI has helped to learn seasonal impact on the environment for the last six years. Confusion Matrix, Overall Accuracy, and Kappa Coefficient are the quality measures used to legitimise the classification exactness.

Inspec keywords: vegetation; learning (artificial intelligence); irrigation; geophysical image processing; ecology; image classification; global warming; remote sensing

Other keywords: global warming analysis; seasonal impact; weather forecasting; BRPI; satellites; innovative method; escalating field; supervised image classification; surface segmentation; supervised aspect; seasonal changes; physical characteristics; coastal studies; principle remote sensing applications; band ratio phenology index method; multispectral imagery; seasonal change analysis; vegetation assessment; ecology assessment; irrigation management; classification exactness

Subjects: Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Geophysics computing; Climatology; Image recognition; Computer vision and image processing techniques; Other topics in Earth sciences; Knowledge engineering techniques; Data and information; acquisition, processing, storage and dissemination in geophysics

References

    1. 1)
      • 33. Wong, J., Abrahamowicz, M., Buckeridge, D.L., et al: ‘Assessing the accuracy of using diagnostic codes from administrative data to infer antidepressant treatment indications: a validation study’, Pharmacoepidemiol. Drug Saf., 2018, 27, (10), pp. 11011111.
    2. 2)
      • 37. Palchowdhuri, Y., Valcarce-Dineiro, R., King, P., et al: ‘Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK’, J. Agric. Sci., 2018, 156, (1), pp. 2436.
    3. 3)
      • 26. ‘Geometric Correction’. Available at http://wtlab.iis.u-tokyo.ac.jp/wataru/lecture/rsgis/rsnote/cp9/cp9-4.htm, accessed September 2019.
    4. 4)
      • 5. Li, X., Wang, L., Cheng, Q., et al: ‘Cloud removal in remote sensing images using nonnegative matrix factorization and error correction’, ISPRS J. Photogramm. Remote Sens., 2019, 148, pp. 103113.
    5. 5)
      • 23. Wang, C., Shu, Q., Wang, X., et al: ‘A random forest classifier based on pixel comparison features for urban LiDAR data’, ISPRS J. Photogramm. Remote Sens., 2019, 148, pp. 7586.
    6. 6)
      • 21. Karami, M., Westergaard-Nielsen, A., Normand, S., et al: ‘A phenology-based approach to the classification of Arctic tundra ecosystems in Greenland’, ISPRS J. Photogramm. Remote Sens., 2018, 146, pp. 518529.
    7. 7)
      • 31. Sivabalan, K.R., Ramaraj, E.: ‘Band value based reflective image classification method to classify the satellite image environment’, Int. J. Eng. Technol., 2017, 9, (5), pp. 36303635.
    8. 8)
      • 39. Deng, Z., Zhu, X., He, Q., et al: ‘Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area’, Adv. Space Res., 2019, 63, (7), pp. 21442154.
    9. 9)
      • 3. Farbod, M., Akbarizadeh, G., Kosarian, A., et al: ‘Optimized fuzzy cellular automata for synthetic aperture radar image edge detection’, J. Electron. Imaging, 2018, 27, (1), pp. 013030.
    10. 10)
      • 24. Mahmoudi, F.T., Arabsaeedi, A., Alavipanah, S.K.: ‘Feature-level fusion of Landsat 8 data and SAR texture images for urban land cover classification’, J. Indian Soc. Remote Sens., 2019, 47, (3), pp. 479485.
    11. 11)
      • 38. Cheng, G., Yang, C., Yao, X., et al: ‘When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs’, IEEE Trans. Geosci. Remote Sens., 2018, 56, (5), pp. 28112821.
    12. 12)
      • 28. Ding, H., Shi, J., Wang, Y., et al: ‘An improved dark-object subtraction technique for atmospheric correction of Landsat 8’. Proc. In MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, Enshi, China, December 2015, 9815, p. 98150K1-8.
    13. 13)
      • 15. Raeva, P.L., Sedina, J., Dlesk, A.: ‘Monitoring of crop fields using multispectral and thermal imagery from UAV’, Eur. J. Remote Sens., 2019, 52, (1), pp. 192201.
    14. 14)
      • 2. Belgiu, M., Csillik, O.: ‘Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis’, Remote Sens. Environ., 2018, 204, pp. 509523.
    15. 15)
      • 25. ‘Landsat Dataset’. Available at https://earthexplorer.usgs.gov, accessed September 2019.
    16. 16)
      • 13. Blanchette, M., Rousseau, A.N., Poulin, M.: ‘Mapping wetlands and land cover change with landsat archives: the added value of geomorphologic data’, Can. J. Remote Sens., 2018, 44, (4), pp. 120.
    17. 17)
      • 16. Kussul, N., Mykola, L., Shelestov, A., et al: ‘Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery’, Eur. J. Remote Sens., 2108, 51, (1), pp. 627636.
    18. 18)
      • 18. Yu, Z., Di, L., Tang, J., et al: ‘Land use and land cover classification for Bangladesh 2005 on Google earth engine’. Proc. Int. Conf. Agro-geoinformatics, Hangzhou, China, August 2018, pp. 15.
    19. 19)
      • 35. ‘F1 score’. Available at https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9, accessed September 2019.
    20. 20)
      • 7. Yang, Z., Willis, P., Mueller, R.: ‘Impact of band-ratio enhanced AWIFS image on crop classification accuracy’. Proc. Int. Conf. The Future of Land Imaging Going Operational, Denver, Colorado, November 2008.
    21. 21)
      • 22. Jia, K., Liu, J., Tu, Y., et al: ‘Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain’, Front. Earth Sci., 2019, 13, (2), pp. 327335.
    22. 22)
      • 1. Gargari, A.M., Ozbey, B., Demir, H.V., et al: ‘A wireless metamaterial-inspired passive rotation sensor with submilliradian resolution’, IEEE Sens. J., 2018, 18, (11), pp. 44824490.
    23. 23)
      • 4. Samadi, F., Akbarizadeh, G., Kaabi, H.: ‘Change detection in SAR images using deep belief network: a new training approach based on morphological images’, IET Image Process., 2019, 13, (12), pp. 22552264.
    24. 24)
      • 27. Shen, H., Li, H., Qian, Y., et al: ‘An effective thin cloud removal procedure for visible remote sensing images’, ISPRS J. Photogramm. Remote Sens., 2014, 96, pp. 224235.
    25. 25)
      • 34. Sinharay, S., Johnson, M.S.: ‘Measures of agreement: reliability, classification accuracy, and classification consistency’, in ‘Handbook of diagnostic classification models’ (Springer, Cham, 2019), pp. 359377.
    26. 26)
      • 32. Pontius, JrR.G., Millones, M.: ‘Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment’, Int. J. Remote Sens., 2011, 32, (15), pp. 44074429.
    27. 27)
      • 19. Khaliq, A., Peroni, L., Chiaberge, M.: ‘Land cover and crop classification using multitemporal sentinel-2 images based on crops phenological cycle’. Proc. Int. Workshop on Environmental, Energy, and Structural Monitoring Systems, Salerno, Italy, June 2018, pp. 15.
    28. 28)
      • 17. Li, D., Lu, D., Li, N., et al: ‘Quantifying annual land-cover change and vegetation greenness variation in a coastal ecosystem using dense time-series Landsat data’, GIsci. Remote. Sens., 2019, 56, (5), pp. 769793.
    29. 29)
      • 14. Kumar, A., Bhandari, A.K., Padhy, P.: ‘Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing’, IET Signal Process., 2012, 6, (7), pp. 617625.
    30. 30)
      • 8. Suresh, G., Hovenbitzer, M.: ‘Texture and intensity based land cover classification in Germany from multi-orbit & multi-temporal sentinel-L images’. Proc. Int. Conf. Geoscience and Remote Sensing Symp., Valencia, Spain, July 2018, pp. 826829.
    31. 31)
      • 11. Garcia-Salgado, B.P., Ponomaryov, V.I., Sadovnychiy, S., et al: ‘Parallel supervised land-cover classification system for hyperspectral and multispectral images’, J. Real-Time Image Process., 2018, 15, (3), pp. 687704.
    32. 32)
      • 9. Azzari, G., Lobell, D.B.: ‘Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring’, Remote Sens. Environ., 2017, 202, pp. 6574.
    33. 33)
      • 10. Hua, A.K., Ping, O.W.: ‘The influence of land-use/land-cover changes on land surface temperature: a case study of Kuala Lumpur metropolitan city’, Eur. J. Remote Sens., 2018, 51, (1), pp. 10491069.
    34. 34)
      • 29. Liu, J., Wang, X., Chen, M., et al: ‘Thin cloud removal from single satellite images’, Opt. Express, 2014, 22, (1), pp. 618632.
    35. 35)
      • 30. Jiaqing, X., Qi, L., Hongjun, L., et al: ‘Scene classification of remote sensing images based on hierarchical sparse coding’, J. Eng., 2018, 16, pp. 16501657.
    36. 36)
      • 36. ‘Kappa coefficient’. Available at https://www.harrisgeospatial.com/docs/Calculating ConfusionMatrices.html, accessed September 2019.
    37. 37)
      • 12. Brabant, C., Alvarez-Vanhard, E., Morin, G., et al: ‘Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data’. Proc. Int. Conf. Geoscience and Remote Sensing, Valencia, Spain, July 2018, pp. 16361639.
    38. 38)
      • 6. Mwaniki, M.W., Moeller, M.S., Schellmann, G.: ‘A comparison of landsat 8 (OLI) and landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya’. Proc. Int. Conf. Remote Sensing of Environment, Berlin, Germany, May 2015, pp. 897903.
    39. 39)
      • 20. Inglada, J., Vincent, A., Arias, M., et al: ‘Operational high resolution land cover map production at the country scale using satellite image time series’, Remote Sens., 2017, 9, (1), pp. 135.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6526
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6526
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading