###### Abstract
HSI dimensionality reduction can be divided into two categories: feature extraction (FE) and feature selection (FS). FE is to convert high-dimensional data into low-dimensional data through mapping and transformation. The FS of hyperspectral images, that is, band selection, is to select the most effective band subset from all bands, which is essentially a combinatorial optimization problem. The advantage of feature extraction is that it reduces high-dimensional data to low-dimensional in a rapid and direct manner. Its representative methods are Principal Component Analysis (PCA) [5; 6] and Linear Discriminant Analysis (LDA) [7; 8]. Local feature-based manifold learning [9; 10] and sparse representation [11] are also important hyperspectral feature extraction methods. At the same time, feature extraction can also combine two different features of space and spectrum for DR [12]. FE is the goal of reducing dimensionality from a mathematical point of view, but while reducing the dimensionality, it also loses the physical information of ground objects, radiations or reflections contained in the original band. Such results are often not conducive to the classification of hyperspectral remote sensing [13]. The hyperspectral wavelengths obtained by feature selection do not change mathematically, retain the original features and information of the HSI to the greatest extent, and the number of output wavelengths can be controlled by the corresponding adjustment. Therefore, in order to obtain a specific small number of wavelength combinations with a large amount of information, this paper uses the feature selection method. A heuristic algorithm is a common means of feature selection [14; 15; 16; 17]. Among them, Particle Swarm Optimization (PSO) [18] is one of the most popular algorithms to effectively solve the wavelength combination optimization problem because of its advantages of easy implementation and fast convergence. In [16], [PERSON] et al. proposed an improved DR method of Binary Particle Swarm Optimization with Mutation Mechanism (MNBPSO), and used MNBPSO to simultaneously perform band selection and support vector machine parameter determination. The results showed that the method is more accurate than the results obtained by the SVM and SVM+BPSO algorithms. At the same time, subspace decomposition [18; 19; 20; 21] is another representative wavelength selection idea. One of its strategies is to use the \"blocking\" feature of the colormap for the band correlation coefficient matrix to divide all the bands into several subspaces and select high-quality wavelengths from them respectively. In [21], [PERSON] et al. proposed the Improved Optimum Index Factor (IOIF). By selecting the bands with the largest amount of information from different subspaces, IOIF can quickly select the bands with the largest amount of information and the smallest correlation, effectively avoiding the phenomenon of band-centralized selection,reducing the band base participating in the optimal exponential operation and improving the efficiency of the algorithm. However, the two methods mentioned above are based on a single criterion for wavelength selection because the spectral characteristics between wavelengths of objects with similar colors images are more similar and correlated than ordinary objects images. The DR of a single standard can easily select wavelengths with concentrated bands, large information redundancy, and small differences in spectral characteristics, resulting in a decrease in classification accuracy. In order to satisfy the selected wavelength subsets with less correlation, more information and greater spectral differences are required between ground objects at the same time. Based on the information calculation model of the local band index (LBI) in the IOIF method and heuristic algorithm for wavelength selection, this paper proposes a new HSI dimensionality reduction method: LBI-BPSO (Binary Particle Swarm Optimization with Local Band Index).