Application Study On High Resolution Remote Sensing Image Forest Vegetation Classification With Artificial Neural Network

The classification on the remote sensing image is the indispensable content to the forest resource investigation and supervisal. The classification precision influences application level and practicality value of the remote sensing data directly. With the development of remote sensing technology, the sensors used on planets can gain high resolution digital image. In the high resolution remote sensing image, the details of objections are more clear than TM and MSS images, showing great texture and structure information. The key question in the research on high resolution remote sensing image is how to identify the image from many kinds of objections and meet stated precision, which has very important meaning. Base on the classification research of the forest vegetation both here and abroad, the text uses radial basic function neural network method and BP neural network method to analysis and study the types of selected sample on the area of Guangxi province quick bird image. The text picks up the forest pixels in research area using Neural Network method, and compares the precision with conventional mode identify method (least distance method). The main research job and result as follows:(l)This text uses radial basic function neural network method and BP neural network method to pick up the forest pixels on the quick bird image, and the precision achieves 95.13% and 96.26% each other. The result shows the classification quality is satisfaction using the two methods.(2)The text does main study to some sample types, which have great influence to the pick-up forest pixels such as paddy field, farmland and so on. The text researches the distributing character of the pixels gray value in the paddy field and farmland, and finds out the difference with forest pixels gray value distribution. Making the gray value equal of three...