Roof segmentation from airborne LiDAR by combining region growing with random sample consensus

Roofs of a building have the characteristics of greatly different size, complex shape and uncertain number, and airborne LiDAR point cloud has the characteristics of uneven density, irregular distribution and without any semantic information, which make many existing airborne LiDAR roof segmentation methods ineffective and their applicability and precision still need to be improved. Thus, an airborne LiDAR roof segmentation method combining region growing with random sample consensus is proposed in the paper. Firstly, the robust normal estimation is introduced to calculate point cloud normal, a proposed iterative region growing strategy and random sample consensus are applied to extract many reliable roof patches. Then, an iterative process is performed to merge these roof patches based on their parameters and the idea of inlier selection of random sample consensus(RANSAC), and roof parameters are refined by the process. Finally, the orthogonal distance of points which are not segmented by the previous steps to each roof is calculated, and points are assigned to the corresponding roof with the minimum orthogonal distance and less than the threshold, and the roof segmentation results are refined by voting in the local neighborhood. Multiple representative building point clouds and a group of regional building point clouds are used in the experiment. The results show that the proposed method can effectively segment roofs of buildings with different complexity, and can also effectively segment roofs with small area, the average segmentation correctness is 95.56% and 97.93% by using a roof and a single point as the basic evaluation unit. The results can provide reliable information for applications such as three-dimensional building model reconstruction and point cloud reduction

Monitoring of Nitrogen Indices in Wheat Leaves Based on the Integration of Spectral and Canopy Structure Information

Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring crop nutrients, because the results are likely to be influenced by the canopy structure. In addition, nitrogen application rate is one of the main factors influencing the canopy structure of crops. Strong correlations exist between indices of canopy structure and leaf nitrogen, and thus, these can be used to compensate for the spectral monitoring of nitrogen content in wheat leaves. In this study, canopy structural indices (CSI) such as wheat coverage, height, and textural features were obtained based on the RGB and height images obtained by the RGB-D camera. Moreover, canopy spectral reflectance was obtained by an ASD hyperspectral spectrometer, based on which two vegetation indices—ratio vegetation index (RVI) and angular insensitivity vegetation index (AIVI)—were constructed. With the vegetation indices and CSIs as input parameters, a model was established to predict the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) of wheat based on partial least squares (PLS) and random forest (RF) regression algorithms. The results showed that the RF model with RVI and CSI as inputs had the highest prediction accuracy for LNA, the coefficient of determination (R2) reached 0.79, and the root mean square error (RMSE) was 1.54 g/m2. The vegetation indices and coverage were relatively important features in the model. In addition, the PLS model with AIVI and CSI as input parameters had the highest prediction accuracy for LNC, with an R2 of 0.78 and an RMSE of 0.35%, among the vegetation indices. In addition, parts of both the textural and height features were important. The results suggested that PLS and RF regression algorithms can effectively integrate spectral and canopy structural information, and canopy structural information effectively supplement spectral information by improving the prediction accuracy of vegetation indices for LNA and LNC

Integrated Infrared Radiation Characteristics of Aircraft Skin and the Exhaust Plume

Infrared radiation (IR) characteristics are important parameters for detecting, identifying, and striking military targets in the context of systematic countermeasures. Accurate calculation of IR characteristics for aircraft is significant for the simulation of war situations and the designation of combat strategy. In this work, integrated IR characteristics of aircraft skin and exhaust plume and their interaction are investigated by considering the reflection based on a bi-directional reflectance distribution function and various influence factors such as solar irradiation, ground reflection, aerodynamic heating, and projection radiation from the background. Combined with infrared emission and reflection characteristics of the skin, omnidirectional IR intensity distributions of 3−5 μm and 8−14 μm at different Mach numbers are obtained. The exhaust plume IR characteristic for different waves and wavebands are also investigated by considering the presence or absence of base and the difference in nozzle inlet temperature. On this basis, integrated IR characteristics between the skin and exhaust plume are investigated. Results show that aircraft IR characteristics of 3−5 μm are concentrated in the exhaust plume and high-temperature skin near the exhaust plume, while the signals of 8−14 μm are concentrated in the skin. The research results are expected to supply guidance for better detection and identification of typical flight targets

Donor–Acceptor Conjugated Polymers Based on Indacenodithiophene Derivative Bridged Diketopyrrolopyrroles: Synthesis and Semiconducting Properties

Two indacenodithiophene derivative bridged diketopyrrolopyrroles (DPP), i.e., 2,7-bis­(2,5-bis­(2-decyl­tetradecyl)-3,6-dioxo-4-(thiophen-2-yl)-2,3,5,6-tetrahydro­pyrrolo­[3,4-<i>c</i>]­pyrrol-1-yl)-<i>s</i>-indaceno­[1,2-<i>b</i>:5,6-<i>b</i>′]­dithiophene-4,9-dione (DDPP-PhCO) and 2,2′-(2,7-bis­(2,5-bis­(2-decyl­tetradecyl)-3,6-dioxo-4-(thiophen-2-yl)-2,3,5,6-tetrahydro­pyrrolo­[3,4-<i>c</i>]­pyrrol-1-yl)-<i>s</i>-indaceno­[1,2-<i>b</i>:5,6-<i>b</i>′]­dithiophene-4,9-diylidene)­dimalononitrile (DDPP-PhCN), were developed via intramolecular Friedel–Crafts acylation and Knoevenagel condensation. A series of donor–acceptor (D–A) conjugated polymers were synthesized by Stille or direct arylation polycondensation with these two novel units as acceptors and vinyl or thiophene derivatives as donors. The polymers with DDPP-PhCO as acceptor unit exhibited optical bandgaps (<i>E</i>_g^opt) of ca. 1.2 eV and the highest occupied molecular orbital (HOMO) energy levels of ∼−5.3 eV with the difference less than 0.1 eV, and their lowest unoccupied molecular orbital (LUMO) levels were in the range of −3.73 to −3.91 eV. The polymer based on DDPP-PhCN showed similar HOMO level (−5.29 eV) but remarkably lower LUMO level (−4.21 eV). Top-gate/bottom-contact (TGBC) organic field-effect transistors (OFETs) of all the polymers exhibited ambipolar transport behavior with the highest hole mobility (μ_h) and electron mobility (μ_e) up to 1.09 and 0.44 cm² V^–1 s^–1, respectively, in air. Owing to their favorable molecular orientation and frontier molecular orbital distribution, the polymers based on DDPP-PhCO displayed much higher hole and electron mobilities than that based on DDPP-PhCN

Efficient intervention for pulmonary fibrosis via mitochondrial transfer promoted by mitochondrial biogenesis

Abstract The use of exogenous mitochondria to replenish damaged mitochondria has been proposed as a strategy for the treatment of pulmonary fibrosis. However, the success of this strategy is partially restricted by the difficulty of supplying sufficient mitochondria to diseased cells. Herein, we report the generation of high-powered mesenchymal stem cells with promoted mitochondrial biogenesis and facilitated mitochondrial transfer to injured lung cells by the sequential treatment of pioglitazone and iron oxide nanoparticles. This highly efficient mitochondrial transfer is shown to not only restore mitochondrial homeostasis but also reactivate inhibited mitophagy, consequently recovering impaired cellular functions. We perform studies in mouse to show that these high-powered mesenchymal stem cells successfully mitigate fibrotic progression in a progressive fibrosis model, which was further verified in a humanized multicellular lung spheroid model. The present findings provide a potential strategy to overcome the current limitations in mitochondrial replenishment therapy, thereby promoting therapeutic applications for fibrotic intervention