Land-use Information Extraction Based On The Improved BP Neural Network

With social development and population expansion, the land use reform isbecoming deeper and the types of land use are rapidly changed, therefore how fastand efficiently do we identify and classify the types of land use is the hot spot in landmanagement research. Fortunately, the rapid development of remote sensingtechnology provides a convenience.Back propagation neural network (BP network in short) is one of the most widelyused neural networks. Nevertheless, standard BP network has some drawbacks likeslow convergence speed and tendency towards converge to local minimum, etc. Basedo the MATLAB software, this thesis puts forward a new BP network by improving thestandard BP network in the respects of network structure, learning algorithm, dataprocessing and generalization ability, and then conducts land use classification inMiyun mining area, Beijing. The research includes:1) Some improvements are made according to standard BP network:①Aftermakingdeep research on the structure of BP network, the thesis combines golden sectionmethod with genetic algorithm to obtain the node number of hidden layer andinitial connection weight;②Determine the optimal learning algorithm bydiscussing some frequently used learning algorithms in terms of operationalefficiency and memory requirement;③Normalizethe input data to ensure BPnetwork learning effectively, and de-fuzzy the output data.④Discuss both thefactors that influence the BP network generalization ability and the methods whichcan improve it.2) This thesis conducts land cover classification after preprocessing the RapidEyeremote sensing data in opening mining area of Miyun county, Beijing using theimproved BP network and achieves good results.3) In order to evaluate the classification results comprehensively, this thesiscompares results of improved BP network with those of maximum likelihoodmethod and standard BP network from three perspectives, which are classificationaccuracy, classification efficiency and appearance of classification maps. Theresults prove that the improved BP network proposed in this thesis has more advantages than the other two methods, and can be used as a potentialclassification method in Information Extraction and land use classification.