Optimization of Endmember Extraction in Mixed Pixel Unmixing in Hyperspectral Images
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摘要: 高光谱图像的混合像元分解将原始图像分解为多种纯净地物及相应的丰度，端元提取是混合像元分解的关键技术. 针对传统算法计算速度慢、搜索范围较大的特点，基于改进的ICA （independent component analysis）算法以及优化的候选端元判断方法，提出了一种优化的混合像元分解方法. 首先使用改进的算法优化端元提取方法；然后利用相邻像素的光谱特征和空间特征信息，结合并行算法对候选端元进行优化；最后利用真实的高光谱数据对该方法的性能进行了验证. 验证结果表明：该方法能有效提高端元提取精度，降低复杂度，与经典的端元提取算法N-FINDER相比，准确度提高了3.55%，解混后得到的地物分类精度有了明显改善（总体分类精度提高了2.88%）.Abstract: Hyperspectral unmixing is a process for unmixing the mixed pixels of a hyperspectral image composed of several substances and their corresponding proportions. Extracting endmembers is a major problem in hyperspectral unmixing. Owing to the goal of achieving a large search range for the endmembers, the efficiency of the traditional algorithm is usually low. Based on an improved ICA（independent component analysis）and optimized endmember extraction method, an improved method of unmixing was proposed. Firstly, endmember extraction was optimized by means of an optimized FastICA algorithm. Thereafter, spatial information and spectral information were considered and combined into a multi-core parallel processing to optimize the candidate endmembers. Lastly, the performance of the proposed algorithm was verified using real hyperspectral data. The method was shown to overcome the shortcomings of the traditional method and obtain more accurate endmembers. In particular, compared to the accuracy of the traditional N-FINDER method, the accuracy of the extracted endmembers increases by 3.55%. The object classification accuracy also improves immensely, and the overall classification accuracy is increased by 2.88%.
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图 4 提取端元与参考光谱的比较
Figure 4. Comparison result of extraction endmember and reference spectra
图 6 不同分类方法的总体分类精度对比
Figure 6. Overall classification accuracy comparison of different methods
表 1 提取端元的准确性评价
Table 1. Accuracy assessment of the extracted endmembers
算法 端元1 端元2 端元3 端元4 N-FINDER 0.092 3/0.033 1 0.136 5/0.053 6 0.087 2/0.006 9 0.231 5/0.047 8 IEA 0.090 2/0.032 6 0.137 2/0.049 8 0.080 2/0.006 3 0.232 4/0.047 2 OSP 0.091 1/0.033 4 0.135 6/0.050 8 0.083 4/0.006 2 0.233 4/0.043 6 O-ICA 0.087 2/0.030 1 0.120 6/0.043 1 0.077 9/0.006 1 0.207 2/0.040 5 
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表 2 不同的噪声水平下的运算时间比较
Table 2. Comparison of computation time under different noise contamination levels
算法 噪声/dB 15 20 25 30 35 40 45 50 N-FINDER 1.023 4 0.880 4 0.998 4 1.156 3 0.952 3 1.178 3 0.945 2 1.145 3 IEA 0.093 4 0.186 5 0.145 5 0.095 4 0.156 7 0.086 5 0.123 9 0.185 6 OSP 0.076 8 0.078 3 0.078 9 0.087 6 0.076 5 0.087 6 0.098 7 0.074 3 O-ICA 0.026 7 0.035 3 0.033 4 0.033 5 0.025 4 0.043 2 0.045 1 0.043 4
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