In this paper, author will firstly discuss the possibility of detecting earthquake precursory by means of NOAA data application on the basis of analysis of the conventional earthquake predicting, and further analysis the problems of earth surface temperature and factors related to earthquake on the earth-surface form energy transmission equation and put forward the modify split windows algorithm to inverse the earth surface temperature in earthquake region. Finally author used the NOAA/AVHRR satellite data to inverse the ground surface temperatures (night) before and after Chinese Zhangbei earthquake (Ms6.2). The result indicated that the ground temperatures before 20 days earthquake was a drop trend. Only 3 days before earthquake, the ground temperature in epicenter just began to go up. Entire temperature changing submitted to a drop tendency.
Due to the SAR generates images with coherent processing of the scattered signal, producing the images usually highly susceptible to speckle effects, a new mother wavelet function was constructed and further formed a wavelet function family. It possesses the narrow band in the low-pass and its wave shape distributes on the symmetry of impulsion response coefficient in time domain, which enable it has an excellent performance in dealing with wave signal. With the original ocean SAR image that its size is 1024*1024, the Wiener filter algorithm was adopted and the new wavelet filter was applied in order to suppress speckle and then to exact the edge information with the Prewitt filter algorithm. Compared several popular mother wavelet functions with the created wavelet function as the same processing method and uniform image, the result shows that the new mother wavelet function is better than other ones in the preserving image's edge information, though its performance is not good as well as compared ones in the suppressing speckle. Based on the above discussion, it is obvious that the wavelet filter is a very effective method in speckle filtering and the constructed mother wavelet function plays an important role in extracting SAR image edge feature information.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.