Insat image

Image fusion is the procedure in which two input images are fused so as to develop the image quality. The input images have to be the images of the comparable prospect with assorted superiority measures. The superiority of the output image will be superior to any of the input images. In this paper, satellite image fusion performance based on Wavelet Packet Transform (WPT) is proposed. Two level decomposition WPT is done on two images to obtain sub-images. The ensuing coefficients are fused by new fusion rule to acquire the fused image. The worth of this method has explained by different images such as the INSAT 3D, INSAT 3A, LANDSAT and PAN images. In this paper the proposed WPT based fusion technique is compared with Discrete Wavelet Transform (DWT) based image fusion. Simulation results accomplished that the proposed method performs finer for image fusion when compared with DWT. Image fusion methods made a comparison against DWT and WPT quality and quantity. Investigational output ended that the proposed WPT design carry out finer for image fusion in association with DWT. Keywords • Image fusion • INSAT images • Wavelet packet • Wavelet transform • Nikolov, S., Hill, P., Bull, D., Canagarajah, N.: Wavelets for image fusion. In: Petrosian, A.A., Meyer, F.G. (eds.) Wavelets in Signal and Image Analysis, vol. 19, pp. 213–241. Springer, Dordrecht (2001). • Vekkot, S., Shukla, P.: A novel architecture for wavelet based image fusion. World Acad. Sci. Eng. Technol. 57, 372–377 ...

The devastation caused by a natural disaster like tropical cyclone (TC) is beyond comprehension. Livelihoods are damaged and take years on end to fix. An automated process to detect its presence goes a long way in mitigating a humanitarian crisis that is waiting to occur. With the advent of deep learning techniques, Convolutional Neural Network (CNN) has had significant success in solving image-related challenges. The current study proposes a CNN based deep network to classify the presence or absence of TC in satellite images. The model is trained and tested with multi-spectral images from INSAT-3D satellite obtained from Meteorological & Oceanographic Satellite Data Archival Centre (MOSDAC) of Indian Space Research Organization (ISRO), Government of India, and an average accuracy of 0.99 is obtained with the proposed architecture. The number of parameters trained are only 1.9 million, which is far less than earlier studies. The detection process described in this study can serve as the first step in better predicting TC tracks and intensity for disaster management and to minimize the impacts on human lives and economy of the country. Keywords • Tropical Cyclone • Convolutional Neural Network • Cross-entropy loss • MOSDAC • Remote Sensing • Emanuel, K.: Tropical cyclones. Annu. Rev. Earth Planet. Sci. 31, 75–104 (2003). • Smith, A.: The Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019) (2021) • Knutson, T., et al.: Tropical Cyclone...

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