Study On Forest Classification By ENVISAT ASAR Image

There are not only macroscopical decision-making but also microcosmic investigation and monitoring needed in the course of national forestry investigation and monitoring. Remote sensing technology has become a powerful tool to solve the problem. But the visible light remote sensing technology is limited to receive the data because of the weather or light condition. With its all-weather, all-time capabilities, radar remote sensing has great potential for forestry management. In this paper we try to study new suitable method for forestry identification using multi-temporal ASAR data. From this study, we have got the following conclusions:(1) The multi-temporal ASAR image is practicable to forestry classification an it will be better with it's dual polarization and multi-look angle characters(2) The classification accuracy is low if we use the maximum likelihood method to directly classify the pretreatment ASAR image. So it is necessary to use the operation of single or multitemporal ASAR image to acquire more information. So the accuracy will be improved.(3) The oblique classification tress have several advantages for remote sensing applications by virtue of their relatively simple, explicit and intuitive classification structure. In addition, decision tree algorithms are strictly nonparametric and, therefore, without assumptions regarding the distribution of input data the methods are flexible to general classifications among input features and class labels.(4) H- a polarimetric decompositon and classification technique have been applied to ENVISAT ASAR APS data for forest and non-forest classification, some encourage results have been achieved.