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中文摘要：
针对高光谱图像中无背景和目标先验信息情况下的目标检测问题，给出了一种基于量测重构光谱混合模型的目标检测算法．通过构造投影算子削弱背景干扰，提高自动搜索目标光谱特征的准确性；对光谱空间进行估计后，构造量测重构光谱混合模型；以此量测重构混合光谱模型为基础，使用投影抑制背景并提高信噪比以改善检测效果．同时给出了目标信号与局部杂乱背景之间的均方根误差SLCR及目标信号峰值与局部杂乱背景均值的比例PSLCMR两个检测评价指标的定义．利用可见光／近红外波段高光谱图像进行了实验，实验结果和理论分析表明了算法的有效性．
英文摘要：
There presents a detection algorithm based on spectral mixing model reconstructed from measurement in this paper in order to detect unknown targets in unknown environment. Firstly, we project the hyperapectral imagery to suppress the background interference in order to search target spectral more accurately. Then, we estimate the spectral subspace and construct a spectral mixing model reconstructed from measurements, And based on the proposed spectral mixing modeling, we project the hyperspectral imagery, which suppress spectral signatures of background and improve the SNR, in order to increase the detection power. Finally, the Signal to Local Clutter RMSE （SLCR） and Peak Signal to Local Clutter Mean Ratio（ PSLCMR）, which is proposed, are used to evaluate the detection. Theoretic analysis and the results of experiment on visible/near-infrared hyperspectral imagery verify the effectiveness of the algorithm.
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