Typhoon Center Location Research Based On Multi-Channel Satellite Cloud Image

Typhoon is one of the most serious natural disasters all over the world. China is one of countries that are seriously suffered from typhoon. In many east and south provinces of China, typhoon has threatened the industry and agriculture and people's lives and properties.The problem how to face and avoid this disaster or how to turn the harm into the benefits is very important to our country and people's safety. Typhoon forecasts become one of the most important things of weather forecasts. Center location of typhoon is very important to typhoon forecasts.Stationary satellite cloud images can continuously monitor the tropical cloud system, they can also find new weather facts. It is one of the most important tools in typhoon analyzing and forecasting. Center location of typhoon is studied by the stationary satellite cloud images.Three main research works have been done as follows:(1)Multi-channel satellite cloud image fusion based on NonSubsampled Contourlet Transform (NSCT) combined with energy entropy. Image fusion is one of the most important processing for locating center. A multi-channel satellite cloud image fusion algorithm is proposed by combining NSCT with energy entropy. NSCT has characteristics of good multi-resolution, shift-invariance and high directionality. It can give an asymptotic optimal representation of edges and contours in image. The local energy is robust in the representing and locating of all kinds of image features. In this paper, NSCT is used to perform a multi-scale decomposition to a stationary satellite cloud image. Secondly, the local energy and the local energy entropy of the high and low frequency-coefficients are calculated separately. Then we choose new coefficients based on the weighting coefficients, which is calculated by energy entropy. Finally, the fused image is generated by reverse NSCT. Compared with Wavelet Transform, Contourlet Transform and NSCT, the issue regarding evaluation of fusion result is also discussed. Some image fusion examples illustrate that the proposed algorithm in this paper gets abundant information of direction and great robustness to noise.(2) Auto-recognition of typhoon cloud based on boundary features.In different development stages, typhoon has different characteristics such as texture, shape, area, and so on. We couldn't auto-recognize the typhoon clouds in all the stages based only these features.While in these developing stages, typhoon all has helicity but non-typhoon has not. Based on this, we extract boundary features of clouds and statistic the rotation degree of boundary clouds in the fused satellite cloud image.In this paper, we use curvature curve of Bezier histogram to obtain two segmentation thresholds to respectively segment the fused satellite cloud image, and combine typhoon's geometric features, such as rotation, area and shape to automatically recognize the typhoon. Experiment results show that typhoon can be efficiently recognized by the proposed algorithm in all different developing stages.(3)Center locating of typhoon based on fractal and texture features.Various periods of typhoon all have a region with dense cloud, except parts of latter disappearance typhoon cloud. No matter what kind of typhoon, center is located in the region of the dense cloud. Based on this,following work has bee done to get typhoon center:At first, we combine the fractal dimension and Gray-Gradient Co-occurrence Matrix of three second statistical parameters to locate dense cloud region of typhoon; Then, based on center of typhoon has the most abundant Gradient information in region of the dense cloud, we get the center with Gauss and Canny algorithm.Whether typhoon has an Eyed or not, the center region of typhoon all can be located by the proposed algorithm, and high location accuracy can be obtained.