基于光谱差异均衡区间筛选的高光谱目标检测

Hyperspectral Target Detection Based on Balanced Distance Sub Spectra Selection

  • 摘要: 如何快速、准确地进行目标检测,是高光谱遥感图像在实际应用中面临的关键问题.波段选择是提高高光谱数据利用效率的途径之一,针对目前基于光谱匹配的高光谱目标检测算法数据利用率低,易受冗余信息干扰导致检测率不理想的问题.在构建光谱区间差异均衡化计算模型的基础上,提出差异均衡化的光谱子区间提取方法.使用实测高光谱遥感影像数据集对方法进行验证.结果表明,相比于采用全谱段数据以及其他波段选择方法的目标检测结果,所提出的方法在计算耗时、检测准确率方面均取得更理想的结果,可高效实现高光谱图像的目标检测.

     

    Abstract: Accurate and fast target detection is one of the key problems in hyperspectral image applications.Band selection is an essential step to improve the utilization efficiency of hyperspectral data.The current hyperspectral band selection methods don't consider task correlation,which affects the effectiveness of band selection results in actual target detection tasks.A new spectra equal interval extraction method was proposed based on constructing the spectra interval difference equalization calculation model.Experiments on hyperspectral remote sensing image dataset have confirmed the superiority of the new method.The results show that the proposed method can achieve better results in terms of computational time and accuracy compared with other band selection methods,and can efficiently achieve target detection of hyperspectral images.

     

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