High Resolution RS Image Classification Based On Spatial Knowledge Mining Of Land Use

It is a traditional and complex subject for land-use high-resolution remote sensing image classification There are two main difficulties for this issue:First, how to make the classification results more better for GIS applications; Second, there are a lot of phenomenon of "same object different images" and "same image different objects" in high-resolution RS images, which have made it more difficult. The key to solving the two problems has been search out by studying the human visual interpretation process in image classification:that is using thematic knowledge, especially spatial knowledge in classification process. Thus, the paper is all around these three issues:what kinds of land-use spatial knowledge are need, how to mine such spatial knowledge, and how to apply them for land use image classification.The primary task is establishing the classification model and framework, which can provides an overall strategy for the three core issues above. We take the simulation of human visual interpretation behavior as guidance. First, the concept of land use spatial knowledge and their scale effect were summarized, in order to make sure the types of land-use spatial knowledge needed in this study. What is more important is to analysis the internal correlation mechanisms of these knowledge, for establishing a cognitive pyramid; Second, which algorithms will be chosen to knowledge mining, as well as how to compute realize these algorithms has been discussed; Third, the advantages of ant colony optimization algorithm in the field of simulation of artificial visual interpretation have been analyzed, and it will be presented as image classifier with land use spatial knowledge.The core and basis content in this paper is mining useful land-use spatial knowledge from the GIS database and the RS Images. According to the process of human cognitive habit in image classification, a more gradual and comprehensive various spatial knowledge mining technology have been used by:1) realizing space districting technology using overlay analysis method to update the region of land use with relatively consistent rule, from where can extract typical and representative land use spatial knowledge; 2) using GIS auxiliary data to automatically extract sample knowledge. Comparing to the traditional sampling method using the discrete points statistical, which is sensitive to noises and difficult to understand, this paper designed a method based on spatial data clustering algorithm, which; 3) presenting a algorithm of object geometric knowledge-based multi-scale texture feature extraction. Texture is an important image special knowledge, whereas scales are the major factor to affect its validity. But the traditional method, such as empirical analysis and enumeration analysis, cannot obtain the scale parameter quickly and effectively. This paper analyzed the relationship between texture scale and features shape, and concluded:the texture scale parameter is strong correlation with feature geometry characteristic, and it can be determined based on stable features geometry characteristic from a historical database. Furthermore, the multi-resolution texture descriptors can improve the separability among different land use categories, so that the total number of classification categories can be increased and more in line with the land use requirements. Based on the above, this study first describing the spatial geometry characteristic by MER algorithm, followed extracting texture scale parameters by relevant histogram statistical algorithm; 4) introducing Apriori algorithm for spatial association rule mining of land use. As a mature technology in spatial data mining, Apriori algorithm is automatic and intelligent, so it can make land use association knowledge continuity and adaptability, which fill up a deficiency in traditional study by using artificial induction method; 5) proposing an method for descritping the directional chractistic of each category by MER algorithm and LDM algorithm. It includes two steps:extracting the main direction of each feature polygon by MER algorithm, then, statisting the main direction of each category by LDM algorithm, which can be used as original direction in ant colony algorithm; 6) finally, a classification-oriented knowledge database is established with unified expression of all knowledge above.Spatial knowledge based ant colony optimization classification algorithm is the key step of image classification. The main content is how to realize the organic integration mode among ant colony intelligent optimization algorithm, remote sensing image classification, and land-use spatial knowledge. First of all, the coupling concept between ant system and image classification is described from three aspects:the time-space environment of the ant colony system, classification problem description, and the process of solving. Then, ant colony have been designed as the carriers of land use spatial knowledge, whereas land use spatial knowledge generate some parameters for ant colony classification algorithm, based on it, algorithm model is established, as well as the relevant operators are designed. The improvement and innovation are summarized as follows:1) using the sample distribution knowledge-aided into the of design of initial pheromone of ant colony; 2) designing a set of tabu search operator for neighborhood pixel search, in order to improve the algorithm’s stability and speed; 3) using social information and individual information of direction preference to decision the state transition probability integrally; 4) putting forward a new direction weight operators for state transition operator; 5) defining a membership function to measure the solution similarity on the basis of distance function for category matching operator; 6) introducing the incentive strategy to enhance the pheromone intensity of high-quality solutions and restrain the pheromone intensity of low-quality solutions for pheromone update operator, to help the algorithm converge as soon as possible.Example Analysis is the final but useful step. Changjiang County of Hainan Province has been taken as experimental region, to realize the high-resolution RS image classification of land use using the proposed method in this paper. With the guidance of classification framework, various effective land use spatial knowledge were mining and expressing, then successfully used in ant colony classification algorithm. In addition, some contrast analyzed experiments have been designed. Experiments show that:land use spatial knowledge plays an important role in RS image classification, the image classification method proposed has the following advantages:On the one hand, the application range of GIS data has been extended, and the utilize efficiency of GIS database has been improve; On the other hand, comparing to traditional classification method, its process is more in line with the intelligent behavior of human understanding and recognition, which can achieve more richer categorizes, moreover, as spatial knowledge were used, the classification errors which brought by the phenomenon of "same object different images" and "same image different objects" have efficiently reduced, so the classification result is more reasonable, and more suitable for RS image thematic classification of land use.