We describe an automated remote cyclone detection and tracking approach using heterogeneous data from multiple satellites. Single Earth orbiting satellite has been used in the past to detect and track events such as cyclones but suffer from major drawbacks due to limited spatio-temporal coverage. Our novel approach addresses the challenges in using heterogeneous data from multiple data sources for knowledge discovery, tracking and mining of cyclones. Moreover, it offersbetter detection performance and spatio-temporal resolutions. Our solution is sufficiently powerful that it generalizes to multiple sensor measurement modalities. Our approach consists of: (i) feature extraction from each sensor measurement, (ii) an ensemble classifier for cyclone detection, and (iii) knowledge sharing between the different remote sensor measurements. Our extensive experimental results demonstrate (i) the superior performance of our cyclone detector compared to previous work on preprocessed historical data, (ii) stable performance of our cyclone detector when it is applied on different geographical regions (Western Pacific Ocean and the North Atlantic Ocean), (iii) meaningful knowledge derived from the cyclone detector output, and (iv) the performance quality of our automated cyclone detection and tracking solution closely match the cyclone best track information from the National Hurricane Center.