Classification of Land-Water Continuum Habitats Using Exclusively Airborne Topobathymetric Lidar Green Waveforms and Infrared Intensity Point Clouds

Coastal areas host highly valuable ecosystems that are increasingly exposed to the threats of global and local changes. Monitoring their evolution at a high temporal and spatial scale is therefore crucial and mostly possible through remote sensing. This article demonstrates the relevance of topobathymetric lidar data for coastal and estuarine habitat mapping by classifying bispectral data to produce 3D maps of 21 land and sea covers at very high resolution. Green lidar full waveforms are processed to retrieve tailored features corresponding to the signature of those habitats. These features, along with infrared intensities and elevations, are used as predictors for random forest classifications, and their respective contribution to the accuracy of the results is assessed. We find that green waveform features, infrared intensities, and elevations are complimentary and yield the best classification results when used in combination. With this configuration, a classification accuracy of 90.5% is achieved for the segmentation of our dual-wavelength lidar dataset. Eventually, we produce an original mapping of a coastal site under the form of a point cloud, paving the way for 3D classification and management of land and sea covers.

Robust Pedestrian Detection Based on Multi-Spectral Image Fusion and Convolutional Neural Networks

Nowadays, pedestrian detection is widely used in fields such as driving assistance and video surveillance with the progression of technology. However, although the research of single-modal visible pedestrian detection has been very mature, it is still not enough to meet the demand of pedestrian detection at all times. Thus, a multi-spectral pedestrian detection method via image fusion and convolutional neural networks is proposed in this paper. The infrared intensity distribution and visible appearance features are retained with a total variation model based on local structure transfer, and pedestrian detection is realized with the multi-spectral fusion results and the target detection network YOLOv3. The detection performance of the proposed method is evaluated and compared with the detection methods based on the other four pixel-level fusion algorithms and two fusion network architectures. The results attest that our method has superior detection performance, which can detect pedestrian targets robustly even in the case of harsh illumination conditions and cluttered backgrounds.

Quantitative Deterioration Evaluation of Heavy-Duty Anticorrosion Coating by Near-Infrared Spectral Characteristics

Heavy-duty anticorrosion coatings are applied on the surface of steel bridges for protecting against corrosion. By aging deterioration, the coating is worn from the surface year by year. Appropriate re-painting construction programs should be adopted for the maintenance of the bridges according to the evaluation of wear extent. Experimental studies were conducted with the aim of quantitative estimation of the degree of abrasion of the top coat thickness. It was found that there was a correlation between the top coat thickness and the observed infrared intensity and that this calibration relationship could be used to estimate the top coat thickness.

Development of a Negligible Zero-Drift NDIR Analyzer for Measuring NH3 Emitted from an Urban Household Solid Waste Incinerator

An analyzer for measuring NH3 emitted from a combustion process has been developed based on a simple non-dispersive infrared (NDIR) technique because of its cost-effective benefit. The weakness of the NDIR analyzer due to interference and zero-drift has been overcome. A least-interfering bandpass filter (BPF) was found and manufactured to compensate for the interfering effects of gases emitted from a combustion process (e.g., CO, NOx, SO2, CO2, H2O, HCl, formaldehyde, acetaldehyde and toluene). It was found that there was no significant interference in the least-interfering BPF with respect to gases of concern. Measurement errors by the analyzer were less than 2.5% in a range of 1 to 10 ppmv of NH3 compared to a standard method when the compound was measured in complicated mixing gases. For the zero-drift, using BPFs with identical center wavelength with respect to different incident infrared intensity was found to help minimize the zero-drift of the NDIR analyzer. As a result, the analyzer could cut approximately 19% of zero-drift caused by the aging effect of both IR source and detector. It suggests that the analyzer could be applied for measuring NH3 emitted from combustion processes with good accuracy and reproducibility.

Optimization Algorithm of Infrared-Polarization Image Fusion Based on Fireworks Algorithm

Because of the shortcomings of traditional infrared-polarization image fusion algorithm, such as low intelligence and single optimization index, this paper proposes an intelligent infrared-polarization image fusion optimization algorithm based on fireworks algorithm. Firstly, an improved differential image correction method based on single pixel nonuniformity is proposed to remove the cold reflection. The two-dimensional discrete cosine transform (DCT) is used to reduce the image sensitivity and improve the robustness, and the Stokes vector formula is used to obtain the polarization characteristic image. Secondly, based on the strong complementarity between infrared-intensity image and degree of linear-polarization (DOLP) image and the explosive optimization of fireworks algorithm, the problem model of weighted fusion algorithm is established, and the fitness function based on root mean square error (RMSE) is constructed to calculate the optimal weight of source image. In the fusion experiment of long-wave infrared-intensity image and DOLP image, this method is compared with the common fusion algorithms. The results show that this method can effectively fuse the infrared-intensity and degree of polarization information, and the evaluation indexes of standard deviation, spatial frequency, mutual information, structural similarity, peak signal-to-noise ratio and information entropy of the fusion image are better than the comparison algorithm. In the future, cooperated with the long-wave infrared-polarization imaging system, this method can be applied to improve the infrared detection ability in complex environment.

Infrared intensities of imaginary frequencies: Gas-Phase SN2 Transition States

The gas phase SN2 reaction transition state structures for nine [XCZ_3 Y]^- systems, where X,Y=H,F,Cl and Z = H,F were optimized and their normal modes of vibrations were determined at the QCISD/aug-cc-pVTZ level of theory. Using Quantum Theory of Atoms in Molecules (QTAIM), the atomic charges and atomic dipoles were obtained and used to calculate the Charge – Charge Transfer – Dipolar Polarization (CCTDP) contributions to the imaginary normal mode intensity of transition states. The results show that the imaginary bands are strong, ranging from 1217 to 16086 〖km∙mol〗^(-1), much higher than occurs for most bands found in molecules. For all systems, the CT contribution is responsible for 80% of the total intensity on average. The Charge contributions are slightly higher for transitions states with Z = F. Dipolar polarization contributions are always small. The contributions from the Z atoms are negligible, thus only atoms aligned with the reaction axis X-C-Y contribute to total intensity. All charge transfers were evaluated taking the carbon atom as reference, implying that almost all infrared intensity is determined by electron transfers from the nucleophile and carbon and from carbon to the leaving group. The mechanism of charge transfer revealed by the CCTDP model is consistent with the reaction mechanism itself, which points towards the connection between the imaginary normal mode and the reaction coordinate.