## 6 Conclusions and Future Works  
Similar color ground objects dataset. In order to test the DR effect of the wavelength selection method on the hyperspectral dataset with some spectral bands have similarities and save a lot of human resources and the time cost of labeling hyperspectral data. This paper provides a new idea for building hyperspectral datasets by combining laboratory measurement data and using LMM to compute mixed pixels. This dataset is very flexible, maintains the authenticity of the hyperspectral features of the ground objects, and can quantitatively analyze the impact of mixed pixel abundance and data noise on the classification performance, as shown in Sections 5.2.1 and 5.2.2. A new wavelength selection method is proposed. In order to improve the classification accuracy of HSIs of ground objects with close spectral characteristics. In order to select the wavelength combination with more information, less correlation between bands and good separability between ground objects, a new spectral DR algorithm LBI-BPSO is proposed, which combines the two criteria of information and inter-class separability. The novelty of this study is to propose an improvement on IOIF using inter-class distances. Based on the calculation of the information content by the local band index, the inter-class distance is introduced to measure the inter-class separability of ground objects and a reasonable fitness function is proposed. LBI-BPSO focuses the search direction of the particle swarm in the wavelength region with a large amount of information and can obtain a wavelength combination that takes into account the two DR criteria of greater information amount and better sample separability. Then, comparing four wavelength selection methods LBI-BPSO, Partitioned Relief-F, IOIF and GA-BPSO with the inter-class distance as the objective function, the classification experiments were carried out on the hyperspectral simulation dataset. Furthermore, the performance of the algorithm is analyzed from three perspectives: SNR = 1000, different endmember abundances and different SNR conditions. When the SNR = 1000, the LBI-BPSO classification results are compared with IOIF, the OA is increased by 2.90%, the AA is increased by 2.75%, and the Kappa is increased by 3.91%. The classification effect of LBI-BPSO and Partitioned Relief-F is very close, and LBI-BPSO shows a small advantage. For ground objects of different abundances, LBI-BPSO can achieve the highest overall average score. When the SNR conditions change, LBI-BPSO shows a stronger tolerance to noise. The relative numerical difference \(\delta\) of the classification results are calculated under the two conditions of the highest (SNR = 1280) and the lowest (SNR = 10) SNR. Compared with Partitioned Relief-F, the \(\delta\) values of OA, AA and Kappa coefficients of LBI-BPSO decreased by 4.95%, 5.64% and 8.16%, respectively. Compared with IOIF, LBI-BPSO reduces the values of OA, AA and Kappa coefficients by 33.29% and 35.97% and 57.11%, respectively. Compared with GA-BPSO, LBI-BPSO has lower \(\delta\) values of OA, AA and Kappa coefficients by 21.93%, 25.65% and 40.01%, respectively. The above experimental results show that the classification performance of the LBI-BPSO method combining the two DR criteria of the amount of information and inter-class separability is better than that of Partitioned Relief-F, IOIF and GA-BPSO with a single criterion. The above advantages are beneficial to technical research on the online classification of live images by preselecting wavelengths with prior information. Future Works.The use of image simulation based on hyperspectral measured data and the LBI-BPSO DR method can provide a basis for the future development of low-cost multispectral imaging technology. This will be used in the application scenario of onlineclassification of spectrally similar ground object images.