Research On Optimization Algorithm Of Decision Tree Based Classification Using High Resolution Remote Sensing Image

Classification methods for remote sensing image an important part of the research in remote sensing field, which directly affects the using range and results of the application of remote sensing (RS) data. With the development of the remote sensing technology, the remote sensing science has come a long way on the development of the platforms and the resolution. The classification methods of RS images also have been developed towards multi-method and multi-classifier combination. The classification methods have complemented each other for further studys.This purpose of this paper is to research the classification of remote sensing imanges.It is not hard to find that recently the classification algorithms for high-resolution remote sensing images mainly focus on pixel-based classification ones and in order to improve the accuracy of classification of remote sensing image, some auxiliary information is also need to be integrated,according to the analysis of the classification algorithms of remote sensing imanges and pixel-based classification algorithms of remote sensing image.By the synthesis of pixel-based classification algorithms of remote sensing image, we can find many algorithms exist the problem of "the black-box operation",such as artificial neural networks, support vector machines,therefore the classification results are difficult to be interpreted and classification rules arenot clear.Genetic algorithm and immune algorithm which need many parameters are used in the classification of remote sensing image and the feasibility of these algorithms were affirmed.However, these algorithms need to set a lot of parameters and accurate thresholds, which makes the unstability of the classification accuracy. In this paper, the improved decision tree algorithm is adopted to research the classification of the remote sensing image,which avoid the problem of "black box" as mentioned above, and the classification rules and classification results that coud be clearly explained are acquired.The improved decision tree algorithm for the classification of high-resolution remote reduces the effect of mixed pixels to the classification results, and the classification algorithm of decision tree can deal with discrete data, so it is very suitable to be used into the classification of remote sensing image by introducing other assistant classification information.There are still many deficiencies in the classification algorithm of decision tree at the present stage.The main ones are summarized as follows:(1) The treatment of continuous attributes. Most decision tree algorithms tend to handle the decision tables which are composed by discrete attributes,but in the practical application of the algorithm of decision tree, continuous attributes will arise inevitablely. Therefore the discretization of continuous attributes and their corresponding threshold criteria for the classification is a difficulty for the inductive learning at all times.(2) It is difficult to get the global optimal decision tree with the local non-backtracking heuristic. For example, the Quinlan’s Interaction Detection 3 (ID3) algorithm[30] which uses the information gain is not optimal heuristic.(3) A lot of decision tree algorithms often assume that training data is complete, which results in poor fault tolerance in the application of the decision tree system and the noise of incomplete data samples has the great impact on the classification algorithm of decision tree.For the deficiencies of the above classification algorithm of decision tree, this paper makes some improvement to the decision tree algorithm and builds a RS image classification-oriented optimized model of decision tree by combining the classification characteristics of remote sensing image and the knowledge of classification. The content of this study can be summarized as follows:(1) In the present study, the decision tree algorithms usually use complete samples as training data, leading to the bad classification rusults with the noise of incomplete data samples in practical applications,because the effect of classification algorithm of decision tree strongly depends on the samples selected.In order to improve the shortcomings.Firstly, a characteristics priority mechanism for the control of the selection of nodes is proposed by combining the own characteristics of the classification of remote sensing images. The mechanism is based on the knowledge of images. The priority of characteristics between various types of surface features and the overall image are built, and the control mode between the priority of characteristics and the node selection in the decision tree are designed with the prior knowledge of classification of RS images, then the decision tree is optimized by the rules based on knowledge of RS images.(2) For the shortcomings of ID3 algorithm wich uses information gain entropy as the local non-backtracking heuristic, the idea of simulated annealing algorithm is introduced to reconstruct the decision tree’s heuristic, and the objective function of simulated annealing is obtained by the combination of information gain entropy and the overall image of the fragmentation phase factors. Simulated annealing algorithm has the advantage of jumping out from local optimum at some certain chance, which can help the jumping from local optimum in the decision tree algorithm, avoid the over fitting phenomenon, and optimize the decision tree algorithm based on rules of global RS images.(3)By the establishment of the above two criteria, the model of RS images oriented-optimized decision tree is built, and the logical flow of decision tree is designed in detail.The simulated annealing of the vertical cluster is operated on the samples before node selection of optimized decision tree. Compared with usual simulated annealing clustering,this clustering adds a judgment of the attribute of decision into the clustering process.In other words,simulated annealing process takes the correlation coefficient of the sample into account, the convergence speed of clustering also needs to consider whether the decision-making properties of samples are same. In the vertical clustering process, a factor of small sample is designed to ensure the integrity of the sample, which at the same time guaranteed the rate of sample will not get down too fast and there are enough samples participated in the classification. The vertical clustering of samples further reduces the establishment result of the decision tree’s dependence on samples.By operating the clustering and discretization process on the samples in the vertical clustering using the appropriate clustering algorithm, the data that could be handled by decision tree is obtained. Then the pruning process of the decision tree and the evaluation indexes of algorithm’s performance are desiged.(4)By comparing the construction and classification results of QuickBird and SPOT images of decision tree between optimized decision tree and the ID3 algorithms, we can get the following conclusions:The optimized decision tree algorithm can effectively improve the classification accuracy of RS images with different scales. Although by comparing the construction result of tree of the optimized decision tree and the ID3 algorithms, the optimized decision tree could not make significant improvement to the structure, and even the results of the optimize decision tree algorithm is more complex than the ID3 algorithm in dealing with RS images with some scales,but in fact the complexity of the optimize decision tree algorithm does not affect the efficency of calculation,and by using the rules of the tree constructed of the optimize decision tree algorithm, the accuracy of classification can be improved efficiently.By comparing the accuracy of image classification in the decision tree algorithm,neural network algorithms and maximum likelihood experiment in the optimal scale, the usefullness of the optimized decision tree algorithm are confirmed.In the experiment, there is the shortcoming that the accuracy of classification of small surface features with minor proportion in the total region is not high, so this also shows a clear direction for the further research. How to improve and integrate the method to match the classification of the low-resolution remote sensing image is one of directions of the future research.