高光谱遥感图像波段选择方法研究
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摘要
波段是高光谱遥感图像信息的重要载体,当前高光谱遥感图像波段选择方法存在精度低等问题,提出基于一种高精度的高光谱遥感图像波段选择方法。首先对当前高光谱遥感图像波段选择方法的研究现状进行分析,并建立高光谱遥感图像波段选择方法框架,然后利用贝叶斯判定理论将波段选择框架转换成选择模式,利用训练数据计算后验概率,将后验概率最大作为标准,选择较高光谱遥感图像波段,最后仿真实验结果表明,本文方法可准确高效的对高光谱遥感图像进行波段选择,得到十分理想的高光谱遥感图像波段选择效果。
Band is an important carrier of hyperspectral remote sensing image information. Currently,the band selection method of hyperspectral remote sensing image has some problems,such as low accuracy. A band selection method based on high accuracy is proposed. Firstly,the current research status of band selection methods for hyperspectral remote sensing images is analyzed,and a band selection method framework for hyperspectral remote sensing images is established. Then,the band selection framework is transformed into a selection mode by using Bayesian decision theory,the posterior probability is calculated by using training data,and the maximum posterior probability is used as the standard to select the band of hyperspectral remote sensing images. Finally,simulation experiments are carried out. The results show that this method can select the band of hyperspectral remote sensing image accurately and efficiently,and get a very ideal effect of band selection of hyperspectral remote sensing image.
Band is an important carrier of hyperspectral remote sensing image information. Currently,the band selection method of hyperspectral remote sensing image has some problems,such as low accuracy. A band selection method based on high accuracy is proposed. Firstly,the current research status of band selection methods for hyperspectral remote sensing images is analyzed,and a band selection method framework for hyperspectral remote sensing images is established. Then,the band selection framework is transformed into a selection mode by using Bayesian decision theory,the posterior probability is calculated by using training data,and the maximum posterior probability is used as the standard to select the band of hyperspectral remote sensing images. Finally,simulation experiments are carried out. The results show that this method can select the band of hyperspectral remote sensing image accurately and efficiently,and get a very ideal effect of band selection of hyperspectral remote sensing image.
引文
[1]王子明,赵文杰.对利用独立成分分析的高光谱图像波段选择方法的分析[J].科技经济导刊,2018,26(27):157-159.
[2]仇建斌,李士进,朱跃龙,等.基于曲线形状特征的快速高光谱图像波段选择[J].小型微型计算机系统,2014,35(8):1906-1910.
[3]李士进,常纯,余宇峰,等.基于多分类器组合的高光谱图像波段选择方法[J].智能系统学报,2014,9(3):372-378.
[4]王立国,魏芳洁.结合APO算法的高光谱图像波段选择[J].哈尔滨工业大学学报,2013,45(9):100-106.
[5]周杨,厉小润,赵辽英.改进的高光谱图像线性预测波段选择算法[J].光学学报,2013,33(8):264-271.
[6]何元磊,刘代志,王静荔,等.利用独立成分分析的高光谱图像波段选择方法[J].红外与激光工程,2012,41(3):818-824.
[7]彭艳斌,艾解清.基于谱聚类波段选择的高光谱图像分类[J].光电工程,2012,39(2):63-67.
[8]何元磊,刘代志,易世华.一种新的高光谱图像波段选择方法[J].光电工程,2010,37(9):122-126.
[9]赵春晖,陈万海,杨雷.高光谱遥感图像最优波段选择方法的研究进展与分析[J].黑龙江大学自然科学学报,2007,11(5):592-602.
[10]刘颖,谷延锋,张晔,等.一种高光谱图像波段选择的快速混合搜索算法[J].光学技术,2007(2):258-261,265.
[11]任智伟,吴玲达.基于近邻传播算法的高光谱波段选择[J].舰船电子工程,2018,38(9):163-166.
[12]严阳,华文深,刘恂,等.基于高光谱基本准则的波段选择方法[J].光学技术,2018,44(5):634-640.
[13]杨韩,厉小润,赵辽英,等.一种高光谱图像高精度配准波段选择方法[J].光学学报,2018,38(9):137-143.
[14]吴一全,周杨,盛东慧,等.基于子空间中主成分最优线性预测的高光谱波段选择[J].红外与毫米波学报,2018,37(1):119-128.
[15]彭艳斌,郑志军,潘志刚,等.基于流形波段选择的高光谱图像分类[J].光电子·激光,2016,27(6):670-674.
[16]王立国,赵亮,刘丹凤.基于人工蜂群算法高光谱图像波段选择[J].哈尔滨工业大学学报,2015,47(11):82-88.
[17]阳雄耀,钟平,王润生.基于RTF的高光谱图像去噪方法[J].现代电子技术,2015,38(21):1-5,10.
[2]仇建斌,李士进,朱跃龙,等.基于曲线形状特征的快速高光谱图像波段选择[J].小型微型计算机系统,2014,35(8):1906-1910.
[3]李士进,常纯,余宇峰,等.基于多分类器组合的高光谱图像波段选择方法[J].智能系统学报,2014,9(3):372-378.
[4]王立国,魏芳洁.结合APO算法的高光谱图像波段选择[J].哈尔滨工业大学学报,2013,45(9):100-106.
[5]周杨,厉小润,赵辽英.改进的高光谱图像线性预测波段选择算法[J].光学学报,2013,33(8):264-271.
[6]何元磊,刘代志,王静荔,等.利用独立成分分析的高光谱图像波段选择方法[J].红外与激光工程,2012,41(3):818-824.
[7]彭艳斌,艾解清.基于谱聚类波段选择的高光谱图像分类[J].光电工程,2012,39(2):63-67.
[8]何元磊,刘代志,易世华.一种新的高光谱图像波段选择方法[J].光电工程,2010,37(9):122-126.
[9]赵春晖,陈万海,杨雷.高光谱遥感图像最优波段选择方法的研究进展与分析[J].黑龙江大学自然科学学报,2007,11(5):592-602.
[10]刘颖,谷延锋,张晔,等.一种高光谱图像波段选择的快速混合搜索算法[J].光学技术,2007(2):258-261,265.
[11]任智伟,吴玲达.基于近邻传播算法的高光谱波段选择[J].舰船电子工程,2018,38(9):163-166.
[12]严阳,华文深,刘恂,等.基于高光谱基本准则的波段选择方法[J].光学技术,2018,44(5):634-640.
[13]杨韩,厉小润,赵辽英,等.一种高光谱图像高精度配准波段选择方法[J].光学学报,2018,38(9):137-143.
[14]吴一全,周杨,盛东慧,等.基于子空间中主成分最优线性预测的高光谱波段选择[J].红外与毫米波学报,2018,37(1):119-128.
[15]彭艳斌,郑志军,潘志刚,等.基于流形波段选择的高光谱图像分类[J].光电子·激光,2016,27(6):670-674.
[16]王立国,赵亮,刘丹凤.基于人工蜂群算法高光谱图像波段选择[J].哈尔滨工业大学学报,2015,47(11):82-88.
[17]阳雄耀,钟平,王润生.基于RTF的高光谱图像去噪方法[J].现代电子技术,2015,38(21):1-5,10.