【摘要】：本文研究的方向是基于蟻群優(yōu)化數(shù)據(jù)分類規(guī)則挖掘的遙感影像分類,總體來講是從三個方面討論的,首先是遙感影像分類問題的一般方法和特點；再者是蟻群優(yōu)化的基本原理和數(shù)學模型以及實現(xiàn)的算法流程；最后是蟻群優(yōu)化應用于遙感影像分類所要解決的問題。 關于遙感影像分類,使用了最新的Landsat-8數(shù)據(jù),討論了紋理變換、植被變換、主成分分析、獨立成分分析、地形因子提取和多波段最佳指數(shù)因子提取等問題。把包含光譜、遙感指數(shù)、紋理、地形和線性變換的特征組合成一個多波段文件,作為分類的初始特征集,給出自己的特征選取方案。 關于蟻群優(yōu)化,把雙橋?qū)嶒炞鳛橐?討論了旅行商問題、數(shù)據(jù)挖掘的蟻群優(yōu)化原理、數(shù)學模型和算法流程,重點介紹了Ant-Miner模型,在總結蟻群優(yōu)化一般框架的基礎上,引入新的改進方案。 把遙感影像分類和蟻群優(yōu)化結合來講,具體討論了數(shù)據(jù)離散、規(guī)則構造、規(guī)則剪枝、信息素策略和啟發(fā)策略等方面問題的解決辦法。最后設計實例比較基于蟻群優(yōu)化分類方法與最大似然方法的分類結果,得出結論。
[Abstract]:The research direction of this paper is remote sensing image classification based on ant colony optimization data classification rule mining. Generally speaking, it is discussed from three aspects. Firstly, the general methods and characteristics of remote sensing image classification are discussed. Thirdly, the basic principle and mathematical model of ant colony optimization as well as the algorithm flow are discussed. Finally, the problem that ant colony optimization is applied to remote sensing image classification is solved. As for the classification of remote sensing images, the latest Landsat-8 data are used. The problems such as texture transformation, vegetation transformation, principal component analysis, independent component analysis, terrain factor extraction and multi-band optimal index factor extraction are discussed. The features including spectrum, remote sensing index, texture, terrain and linear transformation are combined into a multi-band file as the initial feature set of classification, and their feature selection scheme is given. Regarding ant colony optimization, taking double-bridge experiment as an example, this paper discusses traveling salesman problem, ant colony optimization principle of data mining, mathematical model and algorithm flow, and mainly introduces Ant-Miner model, on the basis of summarizing the general frame of ant colony optimization, Introduce a new improved scheme. Combining remote sensing image classification with ant colony optimization, the solutions of data discretization, rule construction, regular pruning, pheromone strategy and heuristic strategy are discussed in detail. Finally, an example is given to compare the classification results based on ant colony optimization method and maximum likelihood method, and the conclusion is drawn.
【學位授予單位】：安徽理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751;TP18

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