REVEALING DISTRIBUTION OF MODERN ERUPTION OF CHANGBAISHAN MOUNTAIN TIANCHI VOLCANO BY ERS-2 SAR IMAGE
利用ERS-2 SAR图像纹理分析方法揭示长白山天池火山近代喷发物空间分布特征

The characteristics SAR image and algorithm of orthogonal cross course wavelet translation were introduced. On the basis of the previous work, the continuous wavelet transform, discrete wavelet transform and Mallat fast algorithm of orthogonal cross course wavelet disassemble of numerical images were studied; the BP neural network classification of remote sensing image, including network configuration, BP algorithm, network training and classification were analyzed. The importance of SAR image in classification was discussed. This article compared the outcomes of BP neural network classification with and without vector layer, and elicited that importing vector information such as geological data could supplement the spatial information of objects and reduce effectively the cases such as 'diverse objects with same spectrum' and 'multi spectrum for same objects'. In the Tianchi volcano, Changbaishan Mountain, the data of crater, lithology, chronology, contour, fault, earthquake, gravitation are collected, by digitalization, GIS vector databases were established. TM and ERS 2 SAR images and pant aerial photos were collected also, through the previous handling, the images databases under the Mapinfo platform were established. By TM image the linear structures, water system, the outer edge of Tianchi volcano trachite were extracted. By the sensitivity of SAR images to geomorphology, the distribution of parasitical volcano cone and lava fornix around Tianchi volcano were discussed. With the aerial photos the parasitical lava of Qixiangzhan, Baiyun and Bingchang stage were filtered. At the same time by orthogonal cross course wavelet transform, texture analysis was carried on to ERS 2 SAR images, and the texture characters were filtered. On the basis of the spectrum character of multi band of TM images, multi band algebraic grouping were made, and the factor of texture character factor and band grouping of TM images were input to BP neural network classification model. The good classified results were received. Finally, with the geological data, the eruptible stages of cone forming period of Tianchi volcano, part parasitical eruptible stages and the distribution of recent eruptible materials were obtained. The results will be helpful for the volcanic hazard assessment, volcanic geological mapping and hazard prediction.