Research Of Remote Sensing Ore-finding Method Based On Particle Swarm Intelligence

It is a kind of fast and efficient support way of exploration to utilize modern remote sensing technique to assist mineral resource exploration. There are many issues to be resolved or improved in traditional information extraction approaches because mineralization alteration information is weaker than other information in remote sensing image. Mineralization alteration information extraction is critical technology of remote sensing mineral exploration, so it has important academic and applied significance for improving efficiency and reliability of remote ore-finding to go deep into existing technology, and to apply new technique and new method to remote sensing mineralization alteration information extraction. Research working in this theme, establishing a kind of assist method for remote sensing ore-finding based on particle swarm intelligence computation technique, is based on the 11th Five Years support programs for science and technology development of China and key technologies R&D program of Qinghai Province.Through studying optimization and solving method for large scale combinatorial optimization problem, simplification model is put forward settling for Monte Carlo algorithm property, and manifold characters among optimal solutions are revealed also. The experimental results, applying above-mentioned conclusion to different intelligence computation methods, show that convergence performances of those algorithms are improved obviously. Contrastive experiential results show also that particle swarm intelligence has better search performance than other intelligence computation methods while being applied to solve discrete problems. Therefore, it is treated as kernel technique to be studied in this theme.Searching framework model based on particle warm intelligence is established for two-dimension discrete space on a basis of mechanism study of particle swarm intelligence computation. In the model, points in two-dimension discrete space are assigned gravity to. All of particles fly under gravity action according to probability controlling manner that simulates intelligence life. The new model possesses overall situation which normal methods are absent because it utilizes gravity mechanism. Gravity attenuation mechanism boosts up robustness of model. Consequently, new model is provided with better human indicator.Particle swarm intelligence mixels decomposition method is brought forward through combining searching framework with linear mixels decomposition model. Tentative decomposing search is done firstly utilizing intelligence search algorithm, and then decomposing images utilizing linear mixels decomposition according to search results. This settles some issues existing in general mixels decomposition methods, such as non-controlled property of linear matching, and lack of overall situation and so on. Contrastive experiential results show that decomposition results meet distribution of target landmarks in remote sensing image. New method is with better overall situation, and reserves more remote sensing ore-finding information.Shifting hypothesis of mineralization spectrum character is put forward through analyzing and studying mixture property of mineral spectrum in remote sensing image. Particle swarm intelligence mineralization alteration information extraction method is established through combining the hypothesis with particle swarm intelligence search model. In the meanwhile, new neighborhood-search reference model is added into the new method to enhance ulteriorly overall situation of classification results. On this basis, behavior characteristic of particle swarm intelligence is quantified to established probability distribution model of classification results, and to lay the groundwork for follow-up remote sensing ore-finding.In allusion to remote sensing mineralization alteration information retraction utilizing support vector machine, quick optimization-selecting method using particle swarm intelligence for support vector machine classifier is put forward. Through analyzing influence to classification results produced by two critical parameters of classifier, parameter search method is implemented by way of integer encoding manner and evaluating through k-fold crossover validation. In the meanwhile, two heuristic strategies, two-point-epicenter method and multi-point- barycenter method, are founded for improving search efficiency. All of these reduce time of feature extraction, and improve ulteriorly classification quality. Workflow of remote sensing ore-finding based on particle swarm intelligence is established integrating above-mentioned techniques. New method is applied to several representative mineralization segments of programs. Through field experiments and comparing with data of the known alteration areas, we find that the alteration information is nearly in accordance with the known alteration areas. Alteration points founded newly have alteration phenomenon in some degree. Four metallogenic prediction prospective areas, four mine prospecting target areas and nine new ore-finding clue segments are given through synthesizing other ore-finding materials.