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采用PPI算法改进的一种数学形态学端元提取方法
引用本文:徐君,王彩玲,王丽.采用PPI算法改进的一种数学形态学端元提取方法[J].测绘学报,2019,48(8):996-1003.
作者姓名:徐君  王彩玲  王丽
作者单位:西安航空学院电子工程学院,陕西 西安,710077;西安石油大学计算机学院,陕西 西安,710065
基金项目:国家自然科学基金(61763010);陕西省重点研发计划(一般项目—工业领域2019GY—112)
摘    要:自动形态学端元提取(automated morphological endmember extraction,AMEE)算法将结构元素内最纯像元与混合度最大的像元之间的光谱角距离定义为形态学离心率指数(morphological eccentricity index,MEI)来定量化地表示像元的纯净度。然而作为参考标准的混合度最大的像元在不同的结构元素内也是不同的,尤其是当结构元素内的纯净像元占大多数时,像元的均值光谱将更接近纯像元,此时像元的MEI越高,纯度反而越低。针对这一问题,本文提出一种像元纯度指数(pure pixel index,PPI)算法与AMEE算法相结合的端元提取算法PPI-AMEE。在结构元素内,利用PPI指数代替AMEE算法中的MEI指数来寻找最纯像元。变换结构元素时,只有最纯净的像元始终能够投影到随机生成的直线的两端,其PPI值会不断累计增大,而其他像元的PPI值则无法持续增大。累计记录每个像元的PPI值,直至满足迭代终止条件,最终形成一幅PPI图像,端元将在PPI值较大的像元中选取。PPI-AMEE算法只在相对较小的结构元素内运行PPI算法,然后再结合数学形态学中的膨胀操作对整幅图像进行处理,其同时兼顾了图像的光谱信息和空间信息。最后,采用模拟数据及美国内华达州Cuprite地区的机载可见光/红外成像光谱仪(airborne visible infrared imaging spectrometer,AVIRIS)高光谱数据对提出的PPI-AMEE算法进行试验验证。试验结果表明,PPI-AMEE算法的端元提取精度总体上优于AMEE算法和PPI算法。

关 键 词:高光谱图像  端元提取  纯像元指数  数学形态学
收稿时间:2018-10-30
修稿时间:2019-04-29

An improved endmember extraction method of mathematical morphology based on PPI algorithm
XU Jun,WANG Cailing,WANG Li.An improved endmember extraction method of mathematical morphology based on PPI algorithm[J].Acta Geodaetica et Cartographica Sinica,2019,48(8):996-1003.
Authors:XU Jun  WANG Cailing  WANG Li
Institution:1. School of Electronic Engineering, Xi'an Aeronautical University, Xi'an 710077, China;2. School of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
Abstract:Automated morphological endmember extraction(AMEE) algorithm defines the spectral angular distance between the purest pixel and the most mixed pixel in the structural element as the morphological eccentricity index(MEI) to quantitatively denote the purity of the pixel. However, the most mixed pixels as the reference standard are not the same in different structural elements, especially when the pure pixels account for the majority of the structural elements, the mean spectrum of all the pixels will be closer to the pure pixels. At this time, the higher the MEI, the lower the purity of the pixel. To solve this problem, a novel endmember extraction algorithm is proposed in this paper which combines the pixel purity index (PPI) algorithm with AMEE algorithm and is named PPI-AMEE. In the structural element, the PPI is used to replace the MEI index in the AMEE algorithm to find the purest pixel. When the structural element is transformed, only the purest pixel can always be projected to the two ends of the randomly generated line, therefore the PPI value of the purest pixel will increase continuously, while the PPI value of the other pixels will not increase continuously. The PPI value of each pixel is accumulated and recorded until the iterative termination condition is satisfied, and a PPI image is finally obtained. The endmembers are selected from the pixels with higher PPI value. The PPI-AMEE algorithm runs the PPI algorithm in relatively small structural elements, and then processes the whole image with the expansion operation of mathematical morphology, which takes into account both the spectral and spatial information of the image. In the experiment, AVIRIS hyperspectral data from Cuprite area, Nevada, USA are used to validate the proposed PPI-AMEE algorithm. The experimental results show that the endmember extraction accuracy of PPI-AMEE algorithm is better than that of AMEE algorithm and PPI algorithm on the whole.
Keywords:hyperspectral image  endmember extraction  pure pixel index  mathematical morphology
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