利用光谱解混合的目标检测

Target detection using spectral unmixing

高光谱目标检测中背景信息的统计往往受到目标信息的干扰,而高光谱图像中存在的大量混合像元会进一步加深这一干扰。为了准确统计背景信息、显著降低目标像元对背景统计信息的干扰,提出了一种利用光谱解混合的目标检测算法,通过光谱解混合和目标相似性判断,获取目标端元对应丰度系数,并与光谱夹角系数相结合生成合理的背景加权系数,进行加权约束最小能量算子(CEM)目标检测,从而有效提高混合像元的背景信息统计准确度;利用目标端元对应丰度系数和光谱夹角系数生成初步的目标检测结果,与加权CEM目标检测结果相融合进行进一步优化,有效提高算法稳定性,同时再次提高目标检测精度。实验结果表明:对于模拟高光谱图像和真实高光谱图像,本文算法均得到了较好的目标检测效果,算法稳定性较强,且有效提高了目标检测精度,相比传统CEM算法、基于光谱角的加权CEM算法、归一化丰度系数作为目标结果,AUC值分别平均提高了0.0712,0.0312和0.0150,在高光谱应用中具有较强的实用性。

The statistics of background information in hyperspectral target detection are often interfered by target information,and the presence of a large number of mixed pixels in hyperspectral images will further deepen this interference.In this study,we proposed a target detection algorithm using spectral unmixing to accurately calculate background information and significantly reduce the interference of target pixels on background statistical information.First,we obtained the abundance coefficient corresponding to the tar-get end member by spectral unmixing and target similarity judgment.We combined it with the spectral an-gle coefficient to generate a reasonable background weighting coefficient for weighted constrained energy minimization(CEM)target detection,effectively improving the statistical accuracy of background informa-tion of mixed pixels.Second,we generated a preliminary result of target detection by utilizing the abun-dance coefficient corresponding to the target end member and spectral angle coefficient and fused with the weighted CEM target detection result to optimize further,effectively improving the robustness of the algo-rithm and target detection accuracy.Experimental results showed that the algorithm proposed in this study has good target detection performance for simulated or real hyperspectral images.The algorithm has strong robustness and effectively improves target detection accuracy.Compared with the traditional CEM algorithm,weighted CEM algorithm based on spectral angle,and normalized abundance coefficient as the target result the AUC of this study was promoted by an average of 0.0712,0.0312,and 0.0150,re-spectively.The proposed algorithm has strong practicability in hyperspectral applications.