Analysis And Evaluation Of Feature Used In Scene Classification With Multi-spectral Remote Sensing Image

Remote sensing technology has been developing rapidly after the first landsat sent into outer space. Scene classification,one hotspots of remote sensing , becomes more and more important in the area of national defence and social development. However, having no systemic theoretics guiding how to choose appropriate features has become one of the main limitations in accurate and automatic classification currently. It's exigent to research choosing the effectual and eximious features with analysing the significance and classi- fication ability of feature by multi-spectral or asynchronism remote sensing image.Using the multi-spectral (visible, medium Infrared Ray,long Infrared Ray)remote sensed image of a city in China and some of image which was shot by author,as the main data source,itemize the features used frequently which contain spectral feature alters rapidly following the change of climate or time and texture spectrum expresses the dime- nsional character of image lum. Clustering distribution of average gray has been analysed and assessed. And then the precision of classification with one-spectral and multi- spectral average gray also was analysed by nearest neight rule,a classical supervised classification method.The resu- lts show that the gray information combined with visible and long Infrared Ray(IR) spectral gets the best classification,however long IR with medium IR spectral does the worst. For supervised high-dimensional feature selection, the author presents a three-stage select model, firstly reduces remove the irrelevant and redundant features in the original set, while chooses the required number features at last. The experi- mental results proved the method can drasti- cally reduce the dimension of selected feature set. There also has distinction of classific- ation in selected feature set, therefore a simple feature compositor is presented.For asse- ssing the capability of classification as the time changes, the author analysed the hourly results all-day using the above feature selected model. In addition, distributing curve has been drawing according to the feature species.In the end, this paper concludes by summarizing the research and indicating its fiuture work.