| Autor: |
Kanti Mahanti Sai Kishore, Manoj Kumar Behera, Sujata Chakravarty, Satyabrata Dash |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). |
| DOI: |
10.1109/wiecon-ece52138.2020.9397972 |
| Popis: |
Remote sensing technology is improving day by day which has also increased the uses of hyperspectral imaging tremendously. Exact classification of ground features from hyperspectral images is an important and a popular research area and also have attracted a widespread of attention. A good classification results are achieved by many methods for the classification of hyperspectral imaging. This paper focuses on classification of hyperspectral image using different machine learning techniques like support vector machine (SVM), random forest (RF), polynomial logistic regression (LR), K-Nearest Neighbour (KNN) and decision tree (DT). Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) have been used to reduce the unnecessary and noisy bands present in the dataset. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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