Crop height is an essential parameter used to monitor overall crop growth, forecast crop yield, and estimate crop biomass in precision agriculture. However, individual maize segmentation is the prerequisite for precision field monitoring, which is a challenging task because the maize stalks are usually occluded by leaves between adjacent plants, especially when they grow up. In this study, we proposed a novel method that combined seedling detection and clustering algorithms to segment individual maize plants from UAV-borne LiDAR and RGB images. As seedlings emerged, the images collected by an RGB camera mounted on a UAV platform were processed and used to generate a digital orthophoto map. Based on this orthophoto, the location of each maize seedling was identified by extra-green detection and morphological filtering. A seed point set was then generated and used as input for the clustering algorithm. The fuzzy C-means clustering algorithm was used to segment individual maize plants. We computed the difference between the maximum elevation value of the LiDAR point cloud and the average elevation value of the bare digital terrain model (DTM) at each corresponding area for individual plant height estimation. The results revealed that our height estimation approach test on two cultivars produced the accuracy with R2 greater than 0.95, with the mean square error (RMSE) of 4.55 cm, 3.04 cm, and 3.29 cm, as well as the mean absolute percentage error (MAPE) of 3.75%, 0.91%, and 0.98% at three different growth stages, respectively. Our approach, utilizing UAV-borne LiDAR and RGB cameras, demonstrated promising performance for estimating maize height and its field position.

A sapphire ultrasonic temperature sensor was produced in this study which possessed working stability, antioxidation properties, and small acoustic-signal attenuation. A method was developed to solve the problems of long periods (>0.5 h) and ultrahigh temperature (1800°C) in tests. The sensor adopted here had good sound transmission performance as well as the high thermal conductivity of sapphire single crystals (Al 2 O 3 ), as ultrasonic waveguides. The ultrasonic waveguide was produced by the method of the laser-heated pedestal growth (LHPG method). Calibration experiments in a high temperature furnace found that, at high temperatures and long exposure, sapphire ultrasonic temperature sensors had good stability and repeatability, and it survived in 1600°C for 360 min. This sapphire ultrasonic temperature sensor has potential for applications in aircraft engines where monitoring of high temperatures is very important.

The hybrid digital-analog (HDA) video transmission scheme can be used to avoid the cliff effect and saturation effect. However, directly using HDA in 3D video transmission requires too much bandwidth. This paper addresses synthesis-distortion-aware HDA transmission for 3D videos to improve transmission performance. First, a 3D HDA framework that transmits both texture and depth videos in HDA mode is designed. Second, a recursive synthesis distortion estimation model, called RSDE-3D-HDA, is derived, where the transmission errors of both the texture and depth sequences are considered. Third, we optimize the power allocation between digital and analog signals based on the RSDE-3D-HDA. Finally, simulation results show that our model is accurate and the proposed 3D-HDA achieves better performance in terms of synthesis quality than state-of-the-art methods. INDEX TERMS Hybrid digital-analog video transmission, distortion estimation, 3D video transmission, view synthesis.

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Airborne light detection and ranging (LiDAR) has been recognized as a reliable and accurate measurement tool in forest volume estimation, urban scene reconstruction and land cover classification, where LiDAR data provide crucial and efficient features such as intensity, elevation and coordinates. Due to the complex urban environment, it is difficult to classify land cover accurately and quickly from remotely sensed data. Methods based on the Dempster–Shafer evidence theory (DS theory) offer a possible solution to this problem. However, the inconsistency in the correspondence between classification features and land cover attributes constrains the improvement of classification accuracy. Under the original DS evidence theory classification framework, we propose a novel method for constructing a basic probability assignment (BPA) function based on possibility distributions and apply it to airborne LiDAR land cover classification. The proposed approach begins with a feature classification subset selected by single-feature classification results. Secondly, the possibility distribution of the four features was established, and the uncertainty relationship between feature values and land cover attributes was obtained. Then, we selected suitable interval cut-off points and constructed a BPA function. Finally, DS evidence theory was used for land cover classification. LiDAR and its co-registration data acquired by Toposys Falcon II were used in the performance tests of the proposed method. The experimental results revealed that it can significantly improve the classification accuracy compared to the basic DS method.