高光谱遥感图像端元提取的零空间光谱投影算法

NULL SPACE SPECTRAL PROJECTION ALGORITHM FOR HYPERSPECTRAL IMAGE ENDMEMBER EXTRACTION

端元提取技术是高光谱遥感图像光谱解混的关键.在线性光谱混合分析中,首先引入了高光谱遥感图像经过零空间光谱投影后具有单形体的凸不变性.在此基础上,提出了零空间光谱投影算法,通过设计各种度量和准则,制定不同的单次端元提取策略,灵活地实现算法.经过证明,零空间光谱投影算法是对基于子空间投影距离算法(包括零空间投影距离算法与经典正交子空间投影算法)的进一步延伸,提供了更多的端元提取策略.实验结果表明,零空间光谱投影算法在模拟图像以及真实高光谱遥感图像中都能够有效地提取出图像中的各种端元.

Endmember extraction is the key procedure for spectral unmixing of hyperspectral remote sensing image. In the linear spectral mixture analysis, a convex invariance of simplex was introduced when hyperspectral remote sensing image was projected into null space of spectral signature matrix of endmembers. On the basis of the invariance, a null space spectral projection algorithm （NSSPA） was proposed. One-unit endmember extraction strategies were established to implement the algorithm in a flexible way by designing different metrics and principles. It is proved that the proposed algorithm extends the algorithm based on subspace projection distance, including the classical orthogonal subspace projection （OSP） algorithm and the null space maximal distance algorithm. The algorithm provides diversified strategies for endmember extraction. The experimental results indicate that NSSPA demonstrates excellent performance of endmember extraction both in the simulated and real hyperspectral remote sensing images.