| Autor: |
Lakshminarayanan Kumaragurubaran, Hoang Viet Long, Golden Julie Eanoch, Harold Robinson Yesudhas, Ramasamy Sankar Ram Chellapa, Raghvendra Kumar, Santhana Krishnan Rajan |
| Rok vydání: |
2021 |
| Předmět: |
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| DOI: |
10.21203/rs.3.rs-815510/v1 |
| Popis: |
In this paper, we produce a novel raster dataset depending upon the Sentinel-2 satellite. They envelop over thirteen spectral bands. Our novel data set consists of ten classes within a total of 27000 Geo-referenced and labelled images. Gradient Boosting Model (GBM) used to explore this novel dataset in which the overall prediction and accuracy of 97% is obtained from the support of Graphics Processing Unit (GPU) afforded from Google Colaboratory (Colab). The obtained classification result can provide a gateway for numerous earth observation applications. Here, in this paper, we also elaborate on how this classification model might be applied for a conspicuous change in land cover and how it plays an important role in improving the graphical maps. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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