改进粒子群优化算法的高光谱波段选择

高光谱图像具有数百个连续、狭窄的光谱带，光谱范围跨越可见光到红外光，可提供地物的精细光谱属性，对于地物材质和属性的识别分类具有重要应用价值。针对感兴趣目标选择有限的光谱波段进行传输和处理，对于提升高光谱数据处理时效性、以及设计面向特定应用的实用化光谱仪都具有重要意义。而如何结合目标特征选择最优波段成为在提升处理效率的同时保证目标识别或分类精度的必然要求。因此如何从数以百计维度的高光谱图像中选择出具有较好分类识别能力的波段子集是急需解决的问题。提出基于改进粒子群优化算法的高光谱波段选择方法，该方法区别于传统的粒子群优化算法，引入 “概率突跳特性”，并设定新解的淘汰机制，将“停滞”的新解进行淘汰，提高了算法的全局寻优性能。然后基于目标光谱特征采用了最优波段选择的优化目标函数，通过改进的粒子群优化算法求解目标函数，并将选定的波段子集反馈到支持向量机（SVM）中执行分类应用。采用两个标准的高光谱数据集（Indian Pines, Salinas）对选择出的波段子集进行分类测试，结果表明该方法相较于现有方法具有较高的分类精度，在几种方法中，传统的粒子群算法筛选出的波段效果最差；该算法筛选出的波段的分类精度最好，两个数据集的分类精度分别可以达到98.141 4%和99.084 8%。

Hyperspectral Band Selection Based on Improved Particle Swarm Optimization Algorithm

Hyperspectral images have hundreds of continuous and narrow spectral bands, spanning visible light to infrared light. They can provide fine spectral properties of ground objects and have important application value for recognizing and classifying ground objects’ materials and attributes. It is of great significance to select limited spectral bands for transmission and processing of interested targets, improving the timeliness of hyperspectral data processing and designing practical spectrometers for specific applications. Selecting the optimal band combined with the target features becomes an inevitable requirement to improve the processing efficiency and ensure the accuracy of target recognition or classification. Therefore, selecting the band subset with better classification and recognition ability from hundreds of hyperspectral images is an urgent problem to be solved. This paper proposes a hyperspectral band selection method based on the improved particle swarm optimization algorithm. This method is different from the traditional particle swarm optimization algorithm by introducing the “probability jump characteristic” and setting the elimination mechanism of the new solution to eliminate the “stagnation” new solution, which improves the global optimization performance of the algorithm. Then, based on the spectral characteristics of the target, the objective optimization function of optimal band selection is adopted. The improved particle swarm optimization algorithm is used to solve the objective function, and the selected band subset is fed back to the support vector machine (SVM) for classification application. In this paper, two standard hyperspectral datasets (Indian pines, The experimental results show that the proposed method has higher classification accuracy than the existing methods. Among the several methods, the traditional particle swarm optimization algorithm has the worst effect; the waveband selected by the proposed algorithm has the best classification accuracy, and the classification accuracy of the two data sets can reach 98.141 4% and 99.084 8%, respectively.