Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

- Wanayumini, Opim Salim Sitompul, Saib Suwilo, Muhammad Zarlis


In the field of remote sensing, in addition to the weather forecast, atmospheric dynamics, oceans, cloud cumulonimbus, and Tornado are part of the phenomenon of chaos. Because in the clouds cumulonimbus, there are some layers with a gray border indicating irregular and uncertain. There is a boundary line on the layers of Cumulonimbus Clouds that could be identified based on the pixel where the differences in the intensity values are extremes. A cloud layer cumulonimbus with a gray edge border can be used as the basis for predicting the occurrence of a tornado based on a pixel location that has specific characteristics. In this research, a Supervised Image Classification algorithm with Spectral Angle Mapper was performed to get the minimum and maximum pixel intensity interval values based on spectral angles in cumulonimbus clouds. Spectral angles allow for quick mapping in determining the spectral similarities between two spectrums on cumulonimbus cloud layers. The spectral similarities are calculated by referring to the angle between the spectral forming the same dimensional vector space on the RGB color spectrum. Early detection in cumulonimbus cloud layers will indicate the occurrence of chaos phenomenon, which could be used to predict tornadoes. The results showed that the Spectral Angle Mapper approach gave minimum and maximum pixel intensity values interval of the Average Correlation Angle in the dataset image Cumulonimbus Cloud with a classification accuracy value of 95.83%.


cumulonimbus clouds; tornados; chaos phenomenon; spectral angle mapper; average correlation angle.

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