Research On Classification Of Remote Sensing Image With Texture

For better precision in classification of remote sensing (RS) image, many researchers pay attention to the texture of image. For the sake of dealing with the magnitude and updating quickly remote sensing image data, data mining technology emerging in recent years has become a new technique and method in RS image processing. Texture feature can be seen as local patterns and arrangement of the patterns. Association rules mining can mine the frequent patterns from large database. It is the cut-in of data mining and RS image processing. Because of many factors, the fuzziness and randomness of texture feature in image become significant. Dealing with the fuzzy RS image by fuzzy theory has a good future. Cloud theory based on fuzzy sets gives a new idea for processing fuzzy RS image. It is a creative application in RS image processing.The paper studies the supervised and unsupervised classification of RS image based on texture feature. Two new techniques were adopted in classification of RS image. One is data mining, supervised classification based on combined texture association rules. The other is cloud theory, unsupervised classification based on fuzzy texture feature vector cloud and representation of fuzzy classification region based on cloud model. The innovations of the paper were described as follows.1. Proposing the architecture of data mining of texture image, which is one of the most important matters in image data mining, including the representation model of image, concept of association rule based on pixel, pretreatment of texture image, data mining technique based on mask of texture image counting. The architecture includes all the steps of image data mining, and can mine the association rules of texture image.2. Proposing the combined texture association rules and representation of texture image based on combined texture association rules. By data mining technique based on mask of texture image counting, we can get the frequent patterns of texture image. Experiments validate that the frequent patterns can represent the texture feature of image perfectly. So we can accomplish supervised classification by frequent patterns that were mined from samples of texture image and fuzzy classifier of image. Experiments testify that the algorithm has important theory and application value by lower time complexity and better classification result.3. Aiming at the fuzziness and randomness of RS image, the paper introduces the cloud theory into RS image processing in a creative way. The digital characteristics of clouds well integrate the fuzziness and randomness of linguistic terms in a unified way and map the quantitative and qualitative concepts. We adopt the cloud theory to accomplish vagueness and randomness handling of RS image. After correlativity analysis of texture statistical parameters in Grey Level Co-occurrence Matrix (GLCM), we can abstract a few texture statistical parameters that can best represent the texture feature. Based on the abstracted texture statistical parameters, texture multi-dimensions cloud model can be constructed in micro-windows of image, which can represent the texture feature of RS image. At last, the method of counting the distance between the texture feature vector clouds was proposed and used in unsupervised classification of RS image. Experiments testified that the method can get better precision and the algorithm Convergence speed is quick.4. According to the fuzziness of boundary region from image classification, the representation of the fuzzy region (objects) from image classification was proposed. By morphological erosion, the core part (Cloud-core) of spatial region in classification image can be got. The Cloud-core can represent the characteristics of spatial region in classification image. Based on the Cloud-core, we can get the membership of Cloud-drop and accomplish the digital characteristics of Object-cloud. At last, the similarities between Object-clouds have been proposed and testified the method. Experiments validated that Object-cloud could represent the fuzzy region in image perfectly.