Abstract
Remote sensing (RS) has become one of the vital approaches to get the information directly from the earth’s surface. In recent years, with the event of environmental informatics, RS information has contend a crucial role in several areas of analysis, like atmosphere science, ecology, soil pollution, etc. When monitoring, the multispectral satellite data problem are vital once. Therefore, in our analysis, automatic segmentation has aroused a growing interest of researchers over the past few years within the multispectral RS system. To beat existing shortcomings, we provide automatic semantic segmentation while not losing significant information. So, we use SOM for segmentation functions. Additionally, we’ve got planned a particle swarm improvement (PSO) algorithmic rule for directly sorting out cluster boundaries from SOM. The most objective of this work is to get a complete accuracy of over eighty fifth (OA> 85%). Deep Learning (DL) could be a powerful image process technique, together with RS image.
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References
Lavreniuk, M.S., Skakun, S.V., Shelestov, A.J.: Large-scale classification of land cover using retrospective satellite data. Cybern. Syst. Anal. 52(1), 127–138 (2016)
Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2094–2107 (2014)
Drusch, M., Bello, D., Colin, O., Fernandez, V.: Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120, 25–36 (2012)
Roy, D.P., Welder, M.A., Loveland, T.R.: Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172 (2014)
Stokes, G.M., Schwartz, S.E.: The atmospheric radiation measurement (ARM) program: programmatic background and design of the cloud and radiation test bed. Bull. Am. Meteor. Soc. 75(7), 1201–1222 (1994)
Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A.: Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(5), 778–782 (2017)
Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405–7415 (2016)
Inglada, J.: Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS J. photogram. Remote Sens. 62(3), 236–248 (2007)
Mather, P.M., Koch, M.: Computer Processing of Remotely-Sensed Images: an Introduction. John Wiley and Sons, Hoboken (2011)
Focareta, M., Marcuccio, S., Votto, C., Ullo, S. L.: Combination of Landsat 8 and Sentinel 1 data for the characterization of a site of interest. a case study: the royal palace of Caserta, October 2015
Grant, K., Siegmund, R., Wagner, M., Hartmann, S.: Satellite-based assessment of grassland yields. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 40(7), 15 (2015)
Whitehead, K., Hugenholtz, C.H.: Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unmanned Veh. Syst. 2(3), 69–85 (2014)
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Jadhav, J.K., Sonavale, A.P., Singh, R.P. (2020). Segmentation Analysis Using Particle Swarm Optimization - Self Organizing Map Algorithm and Classification of Remote Sensing Data for Agriculture. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_74
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DOI: https://doi.org/10.1007/978-3-030-34080-3_74
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