针对TM影像波段少,光谱信息相对不丰富的情况,提出了两种结合空间与光谱信息的端元提取算法.首先,提出了基于空间分块的端元提取算法,该算法先对影像进行快速浏览,根据地物分布的复杂程度,确定分块的方案,在分块的基础上通过沙漏算法迅速地提取端元;其次,提出了一种基于空间连续性的端元提取算法,此算法也在分块思路指导下,通过光谱相关能级波形匹配法实现区域的分割,根据先验知识确定端元.最后比较了两种算法,基于空间分块的端元提取算法运算速度快,先验知识少,但是误提端元的概率存在;而基于空间连续性的端元提取算法,精度较高,但是需要先验知识.实验证明,这两种基于空间与光谱相结合的端元提取算法能够有效地解决大区域尺度的多光谱端元提取问题,具有广泛地应用前景.%Based on a few bands and unabundant spectral information of TM remote sensing image, two endmember extraction algorithms are put forward. First, spatial split endmember extraction algorithm, which firstly browses the image, based on the complexity of objects, divides the image into different blocks, then uses hourglass algorithm to extract endmembers. Second,region continuity algorithm, also based on dividing-into-blocks idea, which uses extraction and classification of homogenous object algorithm and spectral correlation energy level matching algorithm to extract endmembers. Finally, comparing the two algorithms, spatial split endmembar extraction algorithm runs fast, with little prior knowledge, however, the probability of error extraction endmembers exists; and region continuity algorithm's precision is higher, needs for prior knowledge, and the segment process is slow. Experimental results show that both spatial-and-spectral combined endmember extraction algorithms can effectively solve the large regional scale, multispectral endmember extraction problem, and have broad application prospects.
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