In response to the curse of dimensionality in hyperspectral images (HSIs), to date, numerous dimensionality reduction methods have been proposed among which the feature extraction (FE) methods are of particular interest. This paper introduces a new supervised pixel-based FE called spectral segmentation and integration (SSI). In SSI, the spectral signature curve (SSC) of the pixels are identically divided into some non-overlapping segments, called channels. The existing bands in each channel are then integrated using a mean-weighted operator, leading to some new features in a very lower number than the original bands. SSI applies a particle swarm optimization (PSO) algorithm to globally search and locate the optimum positions and widths of the channels. For the sake of evaluation and comparison, the features provided by the proposed SSI method were applied to the well-known SVM classifier. The results were compared to not only a most recent pixel-based FE method, namely, spectral region splitting but also six conventional FE methods, including nonparametric weighted feature extraction, decision boundaries feature extraction, clustering-based feature extraction, semi-supervised local discriminant analysis, band correlation clustering and principal component analysis. Experimental results, obtained on two HSIs, proved the superiority of the proposed SSI.

响应于高光谱图像（HSI）中的维数的诅咒，迄今为止，已经提出了许多降维方法，其中特征提取（FE）方法是特别令人感兴趣的。本文介绍了一种新的基于监督的基于像素的有限元，称为光谱分割和积分（SSI）。在SSI中，像素的光谱特征曲线（SSC）被相同地划分为一些不重叠的部分，称为通道。然后，使用均值加权运算符将每个通道中的现有频段合并在一起，从而带来一些新功能，其数量要比原始频段低得多。SSI应用粒子群优化（PSO）算法全局搜索并找到通道的最佳位置和宽度。为了评估和比较，所提出的SSI方法提供的功能已应用于著名的SVM分类器。不仅将结果与最新的基于像素的有限元方法（即光谱区域分割）进行比较，还与六种常规的有限元方法进行了比较，包括非参数加权特征提取，决策边界特征提取，基于聚类的特征提取，半监督局部判别式分析，频带相关性聚类和主成分分析。在两个HSI上获得的实验结果证明了所提出的SSI的优越性。基于聚类的特征提取，半监督局部判别分析，带相关聚类和主成分分析。在两个HSI上获得的实验结果证明了所提出的SSI的优越性。基于聚类的特征提取，半监督局部判别分析，带相关聚类和主成分分析。在两个HSI上获得的实验结果证明了所提出的SSI的优越性。