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Deep Learning (DL) techniques such as convolutional neural networks (CNNs) can achieve profoundly reliable feature extraction results when fitted with large and enough data sets and respective labels. Still, applying CNNs concurrently with insufficient labeled data can be problematic as this leads to overall overfitting. In addition, tropical cyclone intensity estimation has been greatly studied and several techniques have been devised although it remains an open challenge for it needs domain understanding during the extraction of feature vectors and significant pre-processing of diverse sets of parameters. Therefore, in this study, we introduce a novel method for tropical cyclone intensity estimation based on infrared images. The proposed method is trained directly on tropical cyclone infrared (TCIR) hourly satellite images. We performed the principal component analysis (PCA) transformation on these extracted feature vectors to reduce the data dimensionality. Ultimately, the transformed features with their corresponding wind speed (kts) features are analyzed using support vector regression (SVR) to ascertain their existing intensity. The experimental results show a strong linear relationship between the two variables. Thus, it indicates that our proposed approach performed remarkably well with a root-mean-square error of 9.65 knots (kts).
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