波段选择是高光谱图像降维的重要手段,将偏最小二乘法引入到高光谱图像波段选择中来,提出一种基于偏最小二乘法的波段选择方法.首先用偏最小二乘法计算训练集样本的潜在向量,接着分析波段与潜在向量的相关程度以确定各波段对于图像分类的重要程度,最后分析候选波段的相关度,获得最终选择波段.实验结果表明,与其他现有波段选择方法相比,该方法在选取相同波段数的情况下可取得较高的分类精度,同时由于避免了特征子集搜索和大矩阵特征值分解的运算,运算速度更快.%Band selection for hyperspectral imagery is an efficient way to reduce its dimensionality. This paper proposes a new band selection method by introducing partial least squares (PLS) into the selection. First, the latent vectors are calculated with PLS. Then, the bands essential to the classification are determined according to the correlation between the bands and the latent vectors. The final band subset is obtained by analyzing the correlation between original bands in candidates. Experimental results show that, compared with other band selection methods, our method has better accuracy on classification. By avoiding features subset selection and eigenvalue decomposition of large matrix, our method performs much faster and more efficiently than traditional methods do.
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