Study Of Landuse Remote Sensing Classification Methods In Long Kou City Based On Non-spectral Information

With the rapid development of remote sensing technology, remote sensing image plays a decisive function. The precision of classification influences directly the practical value and level of remote sensing technology. But because the visual interpretation has shortcomings such as time-consuming, more human resources and so on, people have paid more and more attentions to the study of computer classification of the remote sensing image. How to enhance the precision of land-use computer classification is also a topic getting more and more concerns. Because the images received by remote sensing satellite only present the collection of information on the two-dimensional plane in natural complex, it's difficult to reach the ideal effect only with remote sensing image to classify these spectral messages. By joint Chinese-German technical project (2007DFB70200) and Shandong provincial natural science foundation (Y2008E10), I explain that using some integrated remote information or some non-spectral information to help computer make classifications, which can enhance the classification precision of one certain or several land categories. The main research contents and results are as follows:(1)On the basis of seeing previous research results, I described the theoretical methods of remote sensing image classification, analyzed several common methods of computer classification, introduced the theory and methods of remote sensing image pretreatment, made the previous pretreatment of remote sensing image, and analyzed the features of remote sensing image and the best optimal band combinations in the study area.(2)On the basis of material collection, I built the training samples and classified land-use of the study area by studying remote sensing image and field survey. By classification, I found remote sensing image easily produce two phenomena: one is called"same object with different spectra", and the other is called"different objects with same spectra". Therefore the essay classified the land category of the former phenomenon into many kinds, and then combined them. It's not easy to distinguish the land category of the latter one, so combining them into one and then reclassifying with the help of non-spectral data.(3)Having discussed the classification precision of remote sensing image supported by different non-spectral information and of different classification methods in Long Kou city. By researching the study area, I found the phenomenon of"different objects with same spectra"mainly is caused by topographic factors. So, this essay combines image with DEM, grade of slope, and changing slope, then supervise and classify them. The classification precision of image combined with DEM is higher, reaching 99.2 percent, and the index of kappa is 0.8692. The image with DEM is classified by decision-tree method and neural network method. The precision of neural network classification is relatively high, with whole precision of 93.4 percent. And the index of kappa is 0.9099. The classification results indicate that according to the factual conditions of the study area, introducing corresponding non-spectral information to change the original spectral information can enhance the classification precision relatively. It has more advancement than the traditional precision classified on spectral information. The neural network classification method is superior to the other two classification methods.