Event-based object detection has attracted increasing attention for its high temporal resolution, wide dynamic range, and asynchronous address-event representation. Leveraging these advantages, spiking neural networks (SNNs) have emerged as a promising approach, offering low energy consumption and rich spatiotemporal dynamics. To further enhance the performance of event-based object detection, this study proposes a novel hybrid spike vision Transformer (HsVT) model. The HsVT model integrates a spatial feature extraction module to capture local and global features, and a temporal feature extraction module to model time dependencies and long-term patterns in event sequences. This combination enables HsVT to capture spatiotemporal features, improving its capability in handling complex event-based object detection tasks. To support research in this area, we developed the Fall Detection dataset as a benchmark for event-based object detection tasks. The Fall DVS detection dataset protects facial privacy and reduces memory usage thanks to its event-based representation. Experimental results demonstrate that HsVT outperforms existing SNN methods and achieves competitive performance compared to ANN-based models, with fewer parameters and lower energy consumption.

Multispectral lidar has become a promising technology with the rise in capability of 3-D spectral imaging. However, the precise acquisition of spectral information is interfered by measurement geometry, namely, incidence angle and detection distance. These issues may cause discrepancy within the spectral information, thus limiting the classification capabilities of multispectral lidar. To fill this gap, a hue-saturation-intensity (HSI) color space-based method for multispectral lidar classification is proposed in this study. The proposed scheme does not require radiometric calibration, as the HSI color space is robust to spectral intensity variations within a single target. In this method, spectral data are transformed from red-green-blue (RGB) color space to HSI color space. The three components of the HSI color space are inputted for the classification. Then, a reference target-based radiometric calibration is conducted for comparison. The complex indoor scene and the random forest classifier are used for the validation. The classification results of using raw RGB data, raw HSI data, calibrated RGB data, and calibrated HSI data are compared. Results show that the raw HSI data outperform the raw RGB data in terms of classification accuracy. In particular, the raw HSI data can correct the classification error caused by the measurement geometry more effectively than the calibrated RGB data. The improvement resulting from using the HSI color space is demonstrated by both the three-wavelength multispectral lidar and the 32-channel multispectral lidar. That indicates that HSI color space is a promising tool for enhancing the classification capability of multispectral lidar.

Combining the multispectral rasterized data and the three-dimensional (3D) lidar point cloud has long been a hot topic in the remote sensing field. This facilitates not only target recognition, land-classification, but also understanding for the ecosystems and environment. To address this problem, the concept of novel multispectral lidar (MSL), which captures multispectral reflectance and accurate spatial traits simultaneously, was proposed in this study. The layout of the instrument was described. Four laser diodes were co-aligned into a single beam. The reflectance spectrum at four wavelengths (covering red-edge region) as well as distance were recorded. In a validation experiment, reflectance at four wavelengths and normal vectors obtained by the MSL system were fully utilized to classify different targets including fresh and sere plants, with an overall accuracy of 85.5%. The novel MSL was demonstrated to have great potentials in land-use classification and vegetation monitoring.