基于光谱解混的城市地物分类研究

高光谱遥感信息提取面临的突出问题是混合像元的广泛存在,如何有效地解译混合像元是高光谱遥感应用的关键问题。混合像元不仅影响地物的识别和分类精度,而且是遥感技术向定量化发展的重要障碍,混合像元分解是解决混合像元问题最有效的方法,能够克服高光谱图像空间分辨率的限制。针对传统混合像元分解算法的缺点,基于优化的候选端元判断方法及端元提取的并行设计方法,提出了一种优化的混合像元分解方法,实现了光谱特征信息和空间特征信息的有机融合。通过模拟高光谱数据和真实遥感图像进行仿真研究,实验结果表明,该方法能得到精确的端元和对应的丰度,获得较好的解混效果,为城市地物分类提供了有力支持。

Investigation on Urban Object Classification Based on Spectral Unmixing

One of the prominent problems in hyperspectral remote sensing is the existing of mixed pixel widely. How to effectively interpret mixed pixels is an important problem of hyperspectral remote sensing applications. It is not only a problem of mixed pixels effects identification and classification precision of objects, but also a major barrier for the development of remote sensing technology. Mixed pixel decomposition, which is the most effective method to solve the mixed pixel problem, can break through the limitation of spatial resolution. Aiming to the shortcoming of the traditional algorithm of mixed pixel decomposition, an improved method of mixed pixels was put forward, which can take account of the spatial correlation of spectral information and spectral information, and multi-core parallel processing method to raise its efficiency. The endmembers were automatically extracted, and the abundance charts corresponding to each endmember were obtained at the same time. The performance of the proposed algorithm was verified by using actual hyperspectral image. The experimental results on simulated and real hyperspectral image demonstrated that the proposed algorithm can overcome the shortcomings of traditional method and obtain more accurate endmembers and corresponding abundance, which can provide a strong support for urban object classification.