Electrooptic modes with fast response and high contrast ratio are highly desirable in modern photonics and displays. Ferroelectric liquid crystals (FLCs) are especially promising for fulfilling these demands by employing photoalignment technology in FLC cells. Three electrooptic modes including surface stabilized FLC (SSFLC), deformed helix ferroelectric (DHF) mode, and electrically suppressed helix (ESH) mode are reviewed with the corresponding electrooptic effects like bi- and multi-stable switching, continuous modulation of grayscale or phase, and high contrast switching. The general operation principles FLC electrooptic modes are described, and then the characteristics of each modes for potential applications are summarized. With the advantages of controllable anchoring energy, the photoalignment provides FLC samples with uniform alignment and high contrast ratio. The fast FLCs with a high resolution and high contrast can be used in the next generation display including field sequential color FLC microdisplays, as well as switchable 2D/3D televisions.

Single-pixel imaging (SPI) is a novel method for capturing high-quality 2D images of scenes using a non-spatially-resolved detector. While implementing conventional SPI, a huge number of illuminated patterns are projected onto the object to reconstruct a sharp image. For a situation in which the object occupies part of the illuminated region, we propose an adaptive regional SPI method (ARSI) to decrease the number of projected patterns. In the ARSI scheme, the object region is adaptively located based on the Fourier slice theorem. Then, the illuminated patterns are projected only onto the object region to facilitate imaging efficiency. Experiments demonstrate that the proposed ARSI method can achieve sharp image reconstruction with a substantial reduction in pattern number, thereby improving imaging efficiency.

Acquiring complete and accurate 3D shape measurement results of translucent objects by fringe projection profilometry (FPP) is difficult because of the subsurface scattering effect. The phase offset introduces geometric errors, and the degraded image contrast leads to incomplete measurement results and random errors. In this research, a high-accuracy 3D shape measurement method for translucent objects based on phase-shifting FPP is proposed. The relationship between fringe period and phase error is investigated to determine the fringe periods. Random errors are suppressed by temporal noise reduction, and the robustness of multi-frequency heterodyne phase unwrapping is improved by increasing the interval of fringe periods along with temporal noise reduction. Geometric errors are compensated for by projecting multi-frequency fringe patterns to establish the relationship between fringe period and depth offset. Experimental results show that the proposed method can acquire complete measurement results and significantly reduce the overall error for measuring translucent objects.

Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods.

The polarization patterns of skylight which arise due to the scattering of sunlight in the atmosphere can be used by many insects for deriving compass information. Inspired by insects’ polarized light compass, scientists have developed a new kind of navigation method. One of the key techniques in this method is the polarimetric sensor which is used to acquire direction information from skylight. In this paper, a polarization navigation sensor is proposed which imitates the working principles of the polarization vision systems of insects. We introduce the optical design and mathematical model of the sensor. In addition, a calibration method based on variable substitution and non-linear curve fitting is proposed. The results obtained from the outdoor experiments provide support for the feasibility and precision of the sensor. The sensor’s signal processing can be well described using our mathematical model. A relatively high degree of accuracy in polarization measurement can be obtained without any error compensation.