Unsupervised band selection algorithm combined with K-L divergence and mutual information
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摘要: 波段选择是重要的高光谱图像降维手段。为了达到降维的目的，提出结合K-L散度和互信息的无监督波段选择算法，并进行了理论分析和实验验证。首先选出信息熵最大的波段作为初始波段，然后将散度与互信息量的比值定义为联合散度互信息（KLMI）准则，选择KLMI值大且信息量也大的波段加入波段子集中，选出信息量大且相似度低的波段集合，最终利用k最近邻分类算法实现了基于最大方差主成分分析算法、聚类算法、互信息算法和本文中方法的真实高光谱数据分类实验。结果表明，本文中的算法总体分类精度和κ系数均达到0.8以上，高于其它算法；大多数地物的分类精度均得到提升，具有较好的分类性能。该算法是一种实用的高光谱图像降维算法。Abstract: Band selection is an important method of dimensionality reduction of hyperspectral images. In order to reduce the dimensionality, an unsupervised band selection algorithm combining K-L divergence and mutual information was proposed. And theoretical analysis and experimental verification were carried out. Firstly, the band with the largest information entropy was selected as the initial band. Then, the ratio of divergence to mutual information was defined as the criterion of joint K-L divergence mutual information (KLMI). The band which has large KLMI and information entropy was selected to band subset. Then bands with large information and low similarity were obtained. Finally, the real hyperspectral data classification experiments based maximum-variance principle component analysis (MVPCA), affinity propagation (AP), mutual information (MI) and the proposed method were realized by using k-nearest neighbor classifier. Experimental results show that, the accuracy of the proposed algorithm is higher than that of other algorithms. The overall classification accuracy and kappa coefficient κ are over 0.8. Classification accuracy of the most objects is improved. The proposed method has outstanding performance on classification and is a practical dimensionality reduction algorithm of hyperspectral image.
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Keywords:
- remote sensing /
- band selection /
- K-L divergence /
- mutual information /
- classification
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Figure 3. Overall classification accuracy of different samples with various bands
a—5% samples b—10% samples
Table 1 Comparison of band selection results and information
band selection algorithm MVPCA MI AP K-L proposed algorithm band selection results 8, 20, 36, 62, 63, 69, 70, 72, 92, 95 9, 32, 30, 23, 13，89, 57, 97, 100, 110 112, 113, 31, 114, 121，125, 156, 127, 164, 51 15, 49, 33, 87, 21, 63, 101, 99, 71, 56 112, 42, 31, 40, 108, 51, 120, 150, 21, 9 sum of entropy 67.1277 67.4139 67.0094 67.1058 67.7815 
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Table 2 Classification accuracy of various types of objects (5% samples)
class training samples test samples MVPCA MI AP algorithm in this paper alfalfa 23 23 0.53 0.43 0.38 0.94 corn-N 34 1394 0.49 0.65 0.64 0.71 corn-M 27 803 0.49 0.55 0.64 0.69 corn 25 212 0.37 0.40 0.47 0.66 grass-M 27 456 0.80 0.83 0.91 0.94 grass-T 26 704 0.90 0.90 0.92 0.99 grass-P 14 14 0.57 0.56 0.86 0.86 hay-W 27 451 0.97 0.98 0.98 1.00 oats 10 10 0.27 0.36 0.60 0.93 soybean-N 30 942 0.62 0.63 0.76 0.84 soybean-M 31 2424 0.64 0.77 0.79 0.81 soybean-C 28 565 0.38 0.52 0.47 0.58 wheat 25 180 0.91 0.89 0.98 0.99 woods 30 1235 0.90 0.93 0.94 0.96 buildings 27 359 0.38 0.53 0.47 0.77 stone 26 67 0.86 0.86 0.90 0.96 OA 0.65 0.71 0.76 0.82 κ 0.61 0.69 0.70 0.81
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Table 3 Classification accuracy of various types of objects (10% samples)
class training samples test samples MVPCA MI AP algorithm in this paper alfalfa 23 23 0.28 0.71 0.7 0.79 corn-N 89 1428 0.64 0.60 0.74 0.74 corn-M 73 830 0.75 0.60 0.71 0.81 corn 66 237 0.49 0.49 0.49 0.64 grass-M 71 730 0.96 0.86 0.88 0.82 grass-T 81 478 0.73 0.92 0.94 0.98 hrass-P 14 28 0.47 0.57 0.83 0.62 hay-W 71 478 0.89 0.96 0.99 0.99 oats 10 10 0.44 0.47 0.58 0.47 soybean-N 76 896 0.76 0.74 0.71 0.72 soybean-M 112 2343 0.66 0.71 0.85 0.87 soybean-C 68 525 0.52 0.47 0.72 0.78 wheat 67 138 0.97 0.93 0.94 0.98 woods 89 1176 0.86 0.90 0.96 0.99 buildings 68 318 0.72 0.45 0.62 0.91 stone 47 46 0.20 0.91 0.88 0.91 OA 0.71 0.72 0.81 0.85 κ 0.63 0.69 0.75 0.82
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