###### Abstract  
Due to their similar color and material variability, some ground objects have similar characteristics and overlap in some bands. This leads to a drop in the classification accuracy of hyperspectral images. To address this problem, we simulated hyperspectral images of vegetation and objects with similar colors by mixed pixel calculation to test the classification performance of the dimensionality reduction method for samples with close spectra. In addition, we proposed a novel wavelength selection algorithm called the LBI-BPSO (Binary Particle Swarm Optimization with Local Band Index), which combines the information amount and inter-class separability. The novelty of this study is in its proposal of an improvement of IOIF using inter-class distance. Based on the calculation of the information content by the local band index, the inter-class distance was introduced to measure the inter-class separability of ground objects, and a reasonable fitness function is proposed. It can obtain the wavelength combination of two DR criteria, which considers the larger amount of information and better sample separability. The classification performance of the simulation dataset is verified by comparing LBI-BPSO with Partitioned Relief-F, IOIF (Improved Optimum Index Factor) and GA-BPSO (Particle Swarm Optimization with a Genetic Algorithm). Under the conditions that the signal-to-noise ratio is 1000, compared with IOIF, the OA of LBI-BPSO improved by 2.90%, the AA improved by 2.75%, and the Kappa coefficient improved by 3.91%. LBI-BPSO also showed the best results in the analysis of different abundances and signal-to noise-ratios. The results show that the new wavelength selection algorithm LBI-BPSO, which combines the amount of information and inter-class separability, is more effective than IOIF and GA-BPSO in classifying objects with similar colors and effectively improves the classification accuracy. hyperspectral image classification; wavelength selection; hyperspectral image simulation; objects with similar colors +
Footnote †: journal:  
0000-0001-9991-9999-22000-0001-9999-22000-0001-9999-22000-0001-9999-22000-0001-9999-22000-0001-9999-22000-0001-9999-2200001-9999-2200001-9999-220001-9999-220001-9999-2200  
in hyperspectral images. In [1], [PERSON] et al. proposed a method for hyperspectral image simulation of similar spectra using a distance function. This study concentrates on obtaining similar spectra using distance functions. The Chebyshev distance and spectral angle mapper (SAM) distances are combined to get the advantage of both the distance of vector coordinates and the pattern of the spectra. The simulated hyperspectral image is validated with the AVIRIS image using normalized cross-correlation to obtain each pixel's correlation. The spectral-spatial-based methods aim to extract the spectral and spatial features simultaneously for classification. It can effectively improve the classification accuracy of similar spectral features. In [2], a lightweight spectral-similarity-based SpaAM was designed by [PERSON] et al. to capture the relevant spatial areas. It describes the spatial distribution of homogeneous pixels and interfering pixels implicitly, which weakens the interfering pixels from the neighborhoods of the center pixel. In this module, the spectral similarities between the center pixel and its neighborhoods are measured by the efficient Euclidean distance. The above studies showed that the distance function is a powerful tool for evaluating the spectral similarity of ground objects. Hyperspectral data also contains a significant amount of redundant information. To avoid the Hughes Phenomenon [3; 4], a dimensionality reduction (DR) of HSIs is the first task before classification. Due to the complexity of spectral features, the quality of feature selection will directly affect the classification effect. The research on dimensionality reduction of HSIs has always been an open challenge in the field of hyperspectral applications.