To address the limited spectral dimensionality of single-wavelength active detection and the redundancy of multi-band data in hyperspectral LiDAR (HSL), this paper proposes a recognition method for cat-eye systems based on spectral segmentation and maximum information coefficient (MIC) feature selection.

The experiment is divided into two groups. The first group uses a Hikvision DS-2CD2325E-I day-night hemispheric network camera, and the second uses a Tianzhiyan K811 dashcam as the cat-eye system. Experimental data are collected in an indoor corridor. The target distances to the HSL system range from 2.98 to 15.02 meters, and measurements are repeated at irregular intervals at progressively increasing distances. HSL first captures spectral-spatial point cloud data of cat-eye systems and interference targets. Then, the echo intensity curves are divided into three spectral regions, and representative wavelengths are selected from each based on their characteristics. These selected wavelengths serve as inputs to the support vector machine (SVM) classifier. To enhance classification accuracy and robustness, a feature selection framework based on maximum information coefficient (MIC) is applied, which combines a bootstrap strategy for assessing selection frequency with a redundancy-penalized greedy search to identify key wavelengths. The selected wavelengths are further tested, and the results are compared with those obtained using principal component analysis (PCA), linear discriminant analysis (LDA), random forest (RF), and multilayer perceptron (MLP) methods.

The results show that the wavelength in the fluctuation zone achieved the highest peak classification accuracy in the single-wavelength selection experiment. However, due to the saturation truncation effect of the echo signal, the quantized intensity values of some bands in this region are abnormal, thereby causing the classification accuracy to drop to its lowest level and exhibit severe fluctuations. Despite the near-infrared region exhibiting the highest average accuracy across bands, the actual peak response wavelength of the cat-eye system falls outside this zone. This indicates that the spectral characteristics of this region cannot adequately explain the underlying physical mechanism of the cat-eye effect. Despite having the lowest average accuracy, the visible light region demonstrates the smallest fluctuation range. The results indicate that relying on a single wavelength is difficult to fully characterize the spectral characteristics of cat-eye systems. In the optimization of MIC-based wavelength screening results, the high correlation between wavelengths within the same partition may stem from the small energy difference between waveforms emitted at adjacent characteristic wavelengths. Consequently, the echoes recorded across these channels show high similarity. The constraint of screening based on only a single sample leads to higher echo intensities from the cat-eye system at similar wavelengths, which further increases the spectral similarity of bands within the region. Therefore, each region ultimately retains only one representative wavelength. At the same time, selecting different feature bands for classification, the data shows that cross regional wavelength selection can better reflect the spectral differences between targets, further verifying the rationality of classification based on spectral features. In addition, different feature bands are used for classification. The results indicate that this method can more effectively capture spectral differences between targets, further verifying the theoretical basis of partitioning based on spectral features. The wavelength combination selected from all bands exhibits the poorest classification performance. This is primarily attributed to the absence of partition constraints, which can lead to the selected bands being concentrated in spectrally similar regions. Consequently, these bands exhibit high correlation and provide limited discriminative power, thereby adversely affecting classification accuracy. Therefore, the partitioning strategy avoids spectral redundancy by extracting wavelengths from different regions. In the end, this method only requires 3 wavelengths to achieve effective recognition of cat-eye systems, and the average classification accuracy with SVM reaches 0.858. To verify the applicability of the proposed method in different environments, comparative experiments were conducted in three different scenarios. In dark and low-light scenes, the influence of external light is weak. The signal-to-noise ratio of HSL laser echo signals is higher, and the extraction of spectral features is more stable. Therefore, the method performs best, with an average accuracy of 0.84 and an average F1 value of 0.85. In experimental scenarios with external light interference, the method proposed in this paper can still achieve good target classification, with an average accuracy of 0.77 and an average F1 value of 0.80. Compared with normal lighting scenarios (average accuracy of 0.81 and average F1 value of 0.84), it has decreased by 4.94% and 4.76%, respectively.

The cat-eye system recognition method proposed in this study addresses the problem of poor classification performance in traditional MIC feature selection, which is easily affected by band correlation and sampling instability, by introducing stability screening and redundancy constraints. It achieves robust extraction and combination optimization of key wavelengths, thereby more effectively mining the differential information between hyperspectral bands and improving classification performance. The band selection method in this article outperforms the other four types of feature selection methods in terms of average accuracy and F1 value, and also demonstrates better stability at the lowest accuracy, providing new ideas and solutions for quickly and efficiently identifying high reflection targets.