An Overview of Different Techniques to Provide Semantics to Remote Sensing Data

  • Sumedh Ghavat
  • Parth Kodnani
  • Harshita Singh
  • Jayashree Hajgude
Keywords: Remote Sensing, Convolutional Neural Network, Machine Learning, Deep Learning, Transfer Learning


Due to the rapid advancements in the remote sensing field, there is an immense amount of data being generated. This data is raw. Hence, it requires semantics and one way of providing these semantics is land use and land cover classification. We present different techniques that can be used for the same. After analyzing the different classification models, it was observed that the machine learning models performed poorly as compared to the deep learning models. VGG19 gave the best accuracy of 97.64%. In order to provide semantics to remote sensing data, the different classification models are essential and this work can be further extended into diverse domains.


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How to Cite
Ghavat, S., Kodnani, P., Singh, H., & Hajgude, J. (2021). An Overview of Different Techniques to Provide Semantics to Remote Sensing Data. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 7(1), 159-162.

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