An Overview of Different Techniques to Provide Semantics to Remote Sensing Data
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.
 Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth. 2019, “EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
 Nataliia Kussul, Andrii Shelestov, Mykola Lavreniuk, Igor Butko, Sergii Skakun. 2015, “Deep Learning Approach For Large Scale Land Cover Mapping Based On Remote Sensing Data Fusion”, IEEE International Geoscience and Remote Sensing Symposium.
 Patrick Scha¨fer, Dirk Pflugmacher Patrick Hostert, Ulf Leser. 2018, “Classifying land cover from satellite images using time series analyt- ics”, EDBT/ICDT Joint Conference.
 Xin-Yi Tong, Qikai Lu, Gui-Song Xia and Liangpei Zhang. 2018, “Large-Scale Land Cover Classification in Gaofen-2 Satellite Imagery”, IEEE International Geoscience & Remote Sensing Symposium
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