The Application Research Of Texture Information On Remote Sensing Image Classification

Remote sensing image classification is a hot issue in the field of remote sensing research. At present, the traditional classification of remote sensing classification is combined with spectral information for classification and the image spatial information utilization ratio is low, however,it is proved that spatial information of the remote sensing image is very rich. In order to improve the utilization of remote sensing data, this paper adopts a combination of variogram-based texture information extracted from remote sensing image classification method.Space features of remote sensing image include image shape, size, shadow, texture, location and layout. This paper studies on the image of the texture information and effective use of texture information for the remote sensing image classification has important practical value. First, this paper introduces the status of the image classification methods and the extraction method of image texture information at home and abroad. Next, the paper inruoduces the basic theory of the variogram and two kinds of the image classification methods . The paper discusses the window size of texture, the direction of choice and other issues of detail. Meanwhile, the paper adopts a new method of calculating texture - weighted variogram on the basic of the existing texture extraction algorithm . In image classification methods, the paper chooses two representative classification method, the one is the traditional classification method: maximum likelihood, the other is the computer classification method: BP neural network classification method (the data source is not a strict requirement). In this paper, it is in order to improve the accuracy of the classification to adopt a combination of variogram-based texture information extracted from remote sensing image classification method. The experiments show that combining the classification of texture information, regardless of the classification method used in traditional BP neural network is used for classification accuracy has been improved. At the same time, comparison of two classification process can be seen the classification of BP neural network method is superior to the traditional maximum likelihood classification. This study can be drawn, combined with variogram function information extracted from remote sensing image texture classification can improve the accuracy of image classification.