基于高光谱解混的伪装目标识别技术研究

高光谱遥感图像识别技术在伪装目标识别方面具有很大的应用前景。针对高光谱遥感图像中的混合像元和光谱变异问题，提出基于高光谱解混技术的伪装目标识别方法。该方法采用扩展线性混合模型表征高光谱图像中的光谱变异问题，利用超像元分割技术将原始高光谱图像转换为粗细多尺度特征图，对超像元丰度矩阵附加8-邻域空间加权与行约束，以降低噪声和奇异点像元的影响。针对伪装目标空间分布稀疏的特点，在模型中增加丰度矩阵的截断加权核范数作为正则化项，以提高算法精度。实验结果表明提出的方法具有良好的抗噪性和较高的解混精度，可以有效提高伪装目标识别精度。

Camouflaged Target Recognition Technology Based on Hyperspectral Unmixing

Endmember extraction is the key step in the mixed pixel decomposition of hyperspectral remote-sensing images. Traditional endmember extraction algorithms ignore the spatial correlation and nonlinear structure of hyperspectral images, which restricts their accuracy. To consider the spatial relationship and nonlinear structure of hyperspectral images, a nonlinear endmember extraction algorithm based on homogeneous region segmentation is proposed. A hyperspectral image was divided into several homogeneous regions using a superpixel segmentation method, and the manifold learning method was used to ensure the nonlinear structure of the hyperspectral images, extracting preferred endmembers within homogeneous regions. Simulation data and real hyperspectral image experiments showed that the algorithm proposed herein can guarantee the nonlinear structure of hyperspectral data, and the endmember extraction results were better than those of other traditional linear endmember extraction methods. Even in the case of a low signal-to-noise ratio, effective endmember extraction results were obtained.