热红外港口图像特征的评估器提取与选择
发布时间：2018-10-24 11:10
【摘要】：针对低对比度、条纹噪声、低空间分辨率等特点而导致的热红外图像识别效果不佳问题,提出了一种港口目标热红外遥感图像特征提取与选择方法,实现了一定情况下港口目标的高精度分类。采用纹理、几何等29个特征,通过评估器选择最佳特征组合,并根据识别精度选择最佳分类器,能生成热红外图像港口目标22个最佳分类特征,且具有一定的鲁棒性。经过参数优化后的libSVM(一种支持向量机)分类器分类精度较高;白天图像比夜间图像分类精度更高;像素值、灰度直方图相关的一维和二维统计特征、局部二进制模式特征、边缘方向直方图特征等与灰度和纹理相关的特征对港口目标热红外图像识别影响较大。
[Abstract]:In view of the poor recognition effect of thermal infrared image caused by low contrast, fringe noise and low spatial resolution, a method for feature extraction and selection of thermal infrared remote sensing image of port target is proposed. The high precision classification of port targets is realized under certain conditions. Using 29 features, such as texture and geometry, selecting the best combination of features through the evaluator and selecting the best classifier according to the recognition accuracy, the 22 best classification features of the thermal infrared image port can be generated and have certain robustness. After parameter optimization, libSVM (support Vector Machine) classifier has higher classification accuracy; daytime image classification accuracy is higher than night image classification accuracy; pixel value, gray histogram related one-dimensional and two-dimensional statistical feature, local binary pattern feature, pixel value, gray histogram correlation statistical feature, local binary pattern feature, The features of edge direction histogram such as grayscale and texture have great influence on the thermal infrared image recognition of port target.
【作者单位】： 信息工程大学;国际关系学院;中国测绘科学研究院;
【分类号】：P237;TP751
，
本文编号：2291220
[Abstract]:In view of the poor recognition effect of thermal infrared image caused by low contrast, fringe noise and low spatial resolution, a method for feature extraction and selection of thermal infrared remote sensing image of port target is proposed. The high precision classification of port targets is realized under certain conditions. Using 29 features, such as texture and geometry, selecting the best combination of features through the evaluator and selecting the best classifier according to the recognition accuracy, the 22 best classification features of the thermal infrared image port can be generated and have certain robustness. After parameter optimization, libSVM (support Vector Machine) classifier has higher classification accuracy; daytime image classification accuracy is higher than night image classification accuracy; pixel value, gray histogram related one-dimensional and two-dimensional statistical feature, local binary pattern feature, pixel value, gray histogram correlation statistical feature, local binary pattern feature, The features of edge direction histogram such as grayscale and texture have great influence on the thermal infrared image recognition of port target.
【作者单位】： 信息工程大学;国际关系学院;中国测绘科学研究院;
【分类号】：P237;TP751
，
本文编号：2291220
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