An approach towards automatic intensity detection of tropical cyclone by weight based unique feature vector

Tropical cyclone intensity detection by geometric features of cyclone images and multilayer perceptron

“…7) are also tested on similar type of images and environment. Moreover, the number of images used here is greater than the work done in [10,11]. The proposed method also compared with the work done by Pradhan et al [12], where the performance of deep convolution network is varying from 80.66 to 95.47% and 76.91 to 92.55% depending upon datasets.…”

“…The proposed method is completely based on images and its geometric property with accuracy around 84%. This proposed method is highly inspired by the work done on feature vectors of [10,11]. The detection method is solely based on the images such as ESCS, CS and D which have been analysed in the results section.…”

“…(c) Features extraction from TC images Mean (M), variance (V), density (D), decentricity (DC), area of cyclone (AC) and area of eye (AE) are extracted from a TC image. First, Centre of Gravity (CoG) needs to be calculated based on work done by Onodera [29] for finding the features such as mean, variance (V), density (D), decentrcity (DC) [10,11,29,30]. The following formulas are used on image I of size m × n to calculate the CoG:…”

“…Intensity of TCs is measured from these centres by a specific scale known as tropical cyclone intensity scale [8]. Various other techniques are also established to predict the impact of a TC [9][10][11][12][13][14][15][16]. Dvorak technique [17] is a widely used method to detect cyclones using pattern recognition.…”

“…There are many techniques proposed by researchers towards intensity detection apart from Dvorak and modified Dvorak techniques [17][18][19][20]. Feature-based techniques [11,12,21,22] are mostly focused on the geometric features of the TC images. Machine learning algorithms were introduced to solve many problems like image classification [12].…”