Study Of Classifying Method In Remote Sensing Image

At present, there is a far-reaching use of remote sensing (RS) imagine in the field of land use/land cover change (LUCC) research. The research on LUCC is a core for studies on the global changes. Also the research of spatial-temporal features of land use/land cover change is significantly important for better understanding land use/land cover change and environmental management for sustainable development. The techniques of classification are very important for land use/land cover change (LUCC),so LUCC study are on the basis of the image processing and classification system in the first. How to improve the accuracy of RS interpretation in order to promote the utility of RS technology is a urgent problem in RS application.Compared with conventional statistic classifier, the artificial neural network (ANN) has been developed and applied to remote sensing data classification problem, which doesn't need suppose parameterized distribution of sample space in advance. ANN has complicated mapping capability. The back propagation neural network modal (BP model) is often been used.Back propagation neural network classifier can solve the problems existing in the traditional classifiers and has been gradually used in the classification of remote sensing image. In order to accelerate the training speed, an improved BP method is to be presented after analyzing the BP model. Through setting a training intensity, the training time is reduced. As to the mix pixels, we construct a BP neural network which the nodes of input layer are the bands of remote sensing and the nodes of output layer are percent of several kinds of object. Before classification, Karhunen-Loeve transform is carried through for features abstract. In addition the spectrum information, many ancillary geo-information such as NDVI and Dem is contributed to judge. Synthesizing these methods, we have the test. Compared with classification of MLC method, the results show the improved method has not only the highest accuracy but also the fastest speed of classification.