CLASSIFICATION OF THE EARTH'S SURFACE IMAGE UTILIZING THE HOG AND ANN ALGORITHMS
DOI:
https://doi.org/10.29121/shodhkosh.v4.i1.2023.4110Keywords:
Earth’S Surface Satellite Images, Feature Extraction, Classification, Histogram of Oriented Gradients (HOG), Artificial Neural Network (ANN)Abstract [English]
The crucial motive of this present paper is to probe and classify the globe’s top area picture as the image of the Satellite. On the globe’s surface, it is immensely arduous to plainly classify the hydrosphere and atmosphere, because some on occasions, the two spheres are in the same form, so, it is exceedingly grueling to categorize both spheres. The rationale of classifying this is, the vapor in the atmosphere is cooled by the earth, which is quelled and then as rained. Space explorers therefore need to appraise the quantity of spheres before the precipitation and the amount of spheres after the shower. Here some great ways suggested measuring them minutely. First, picture of the world's surface is taken by satellite; The HOG procedure is utilized to deblocking principal aspects of the image. Then, in the taxonomy algorithms, the most worthwhile ANN approach is utilized for this activity. The paper was therefore elaborated in the hope that the paper would utilize a superior mechanism and give researchers better gratification.
References
Luis Mantilla, “Classification of satellite images using Rp fuzzy c means for unsupervised classification algorithm”, IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI) - 2019. DOI: https://doi.org/10.1007/978-3-030-36211-9_11
Rahul Neware, Amreen Khan, “Survey on Classification techniques used in remote sensing for satellite images”, International conference on Electronics, Communication and Aerospace Technology (ICECA 2018), IEEE Xplore ISBN: 978-1-5386-0965-1.
Roman Melnyk, Yuriy Kalychak, Ruslan Tushnytskyy, “Absolute and Relative Classification of Cloud Regions by Satellite Image Clustering”, International Conference on the Experience of Designing and Application of CAD Systems (CADSM) - 2019. DOI: https://doi.org/10.1109/CADSM.2019.8779333
Dudu Guo, Shunying Zhu, Ji’ao Wei, “Research on Vehicle identification based on high resolution satellite remote sensing Image”, International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) - 2019. DOI: https://doi.org/10.1109/ICITBS.2019.00024
Uthayakumar J, Vengattaraman T, “Performance Evaluation of Lossless Compression Techniques: An Application of Satellite Images”, International conference on Electronics, Communication and Aerospace Technology (ICECA 2018), IEEE Conference Record # 42487; IEEE Xplore ISBN: 978-1-5386-0965-1.
Liang Zhang, Zhenhua Chen, Jian Wang, Zhaodun Huang, “Rocket Image Classification Based on Deep Convolutional Neural Network”, the 10th International Conference on Communications, Circuits and Systems ©2018 IEEE. DOI: https://doi.org/10.1109/ICCCAS.2018.8769176
Alexandra V. Akinina, Michael B. Nikiforov, Alexandr V. Savin, “Multiscale Image Segmentation using Normalized Cuts in Image Recognition on Satellite Images”, 7th Mediterranean Conference On Embedded Computing (MECO), 11-14 June 2018, Budva, Montenegro. DOI: https://doi.org/10.1109/MECO.2018.8406066
Chaomin Shen, Chenxiao Zhao, Mixue Yu, Yaxin Peng, “Cloud Cover Assessment in Satellite Images Via Deep Ordinal Classification”, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium – 2018. DOI: https://doi.org/10.1109/IGARSS.2018.8519035
Y. I. Shedlovska, V. V. Hnatushenko, “A Very High Resolution Satellite Imagery Classification Algorithm”, 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) - ©2018 IEEE. DOI: https://doi.org/10.1109/ELNANO.2018.8477447
T. Postadjian, A. Le Bris, H. Sahbib, C. Mallet, “Domain Adaptation for Large Scale Classification of Very High Resolution Satellite Images with Deep Convolutional Neural Networks”, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - 05 November 2018. DOI: https://doi.org/10.1109/IGARSS.2018.8518799
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Copyright (c) 2023 Dr. K. Venkatasalam, V.Yamini, D. Prasaniya, B. Sanjana
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