多譜遙感圖象分類(lèi)中的特征分析和評(píng)價(jià).pdf

1、華中科技大學(xué)碩士學(xué)位論文多譜遙感圖象分類(lèi)中的特征分析和評(píng)價(jià)姓名：吳凱申請(qǐng)學(xué)位級(jí)別：碩士專(zhuān)業(yè)：模式識(shí)別與智能系統(tǒng)指導(dǎo)教師：曹治國(guó)20070606華華 中 科 技 大 學(xué) 碩 士 學(xué) 位 論 文 VAbstract Remote sensing technology has been developing rapidly after the first landsat sent into outer space. Scene classif

2、ication,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 becom

3、e 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 feat

4、ure 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 th

5、e 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. Clust

6、ering 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 supervis

7、ed 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 supe

8、rvised 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 l

9、ast. 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 composi

10、tor 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 drawi

11、ng according to the feature species. In the end, this paper concludes by summarizing the research and indicating its fiuture work. Key words: Remote sensing Scene classification Feature evaluation Feature selection