A 0.38″ field‐sequential‐color liquid‐crystal‐on‐Silicon (LCOS) microdisplay was developed for mobile projectors. An embedded sensor and heater was integrated onto the LCOS for ensuring a 90% NTSC color at a −20 °C ambient environment. Optical performance and reliability of this LCOS were characterized and discussed.

A photo sensor array was placed on the perimeter of the CS-LCOS microdisplay to capture the incident red, green and blue light in sequence. These light intensities are processed by a peripheral video processor to control the duty cycles of the light sources for an automatic white balance at any time and temperature.

Airborne LiDAR is a popular measurement technology in recent years. Its feature is that it can quickly acquire high precision and high density 3D point coordinates on the surface. The reflective waveform of the radar contains the geometric structure and roughness of the surface reflector. Combined with the information from aerial photographs, it can quickly help users to interpret various surface object types and serve as a basis for land cover classification. The experiment is divided into three phases. In the phase 1, LiDAR data and decision tree classification method (DT) were used to classify the land cover and customize the geometric parameter elevation. In the phase 2, we combined aerial photographs, LiDAR data and DT method to improve the accuracy of land cover classification. In the phase 3, the support vector machine classification method (SVM) was used to compare the classification accuracy of different classification methods. The results show that customizing the geometric parameter elevation can improve the overall classification accuracy. The results of the study showed that the DT method and the SVM method had better results for the grass, building and artificial ground, and the SVM method had better results for the planted shrub and bare ground.