Non-line-of-sight imaging of moving objects obscured by a random corridor

Non-line-of-sight (NLOS) imaging makes it possible to reconstruct hidden objects around corners, which is of fundamental importance in various fields. Despite recent advances, NLOS imaging has not been studied in certain typical random scenarios, such as tortuous corridors filled with random media. We dub such a category of complex environment “random corridor”, and propose a reduced spatial- and ensemble-speckle intensity correlation (RSESIC) method to image a moving object obscured by a random corridor. Experimental results show that the method can reconstruct image of a centimeter-sized hidden object with a sub-millimeter resolution by a low-cost digital camera. The imaging capability depends on three system parameters and can be characterized by the correlation fidelity (CF). Furthermore, the RSESIC method is able to recover the image of objects even for a single pixel containing the contribution of about $10^2$ speckle grains, which overcomes the theoretical limitation of traditional speckle imaging methods. Last but not least, when the power attenuation of speckle intensity leads to the serious deterioration of CF, the image of hidden objects can still be reconstructed by the corrected intensity correlation.

Airborne LiDAR Intensity Correction Based on a New Method for Incidence Angle Correction for Improving Land-Cover Classification

Light detection and range (LiDAR) intensity is an important feature describing the characteristics of a target. The direct use of original intensity values has limitations for users, because the same objects may have different spectra, while different objects may have similar spectra in the overlapping regions of airborne LiDAR intensity data. The incidence angle and range constitute the geometric configuration of the airborne measurement system, which has an important influence on the LiDAR intensity. Considering positional shift and rotation angle deviation of the laser scanner and the inertial measurement unit (IMU), a new method for calculating the incident angle is presented based on the rigorous geometric measurement model for airborne LiDAR. The improved approach was applied to experimental intensity data of two forms from a RIEGL laser scanner system mounted on a manned aerial platform. The results showed that the variation coefficient of the intensity values after correction in homogeneous regions is lower than that obtained before correction. The overall classification accuracy of the corrected intensity data of the first form (amplitude) is significantly improved by 30.01%, and the overall classification accuracy of the corrected intensity data of second form (reflectance) increased by 18.21%. The results suggest that the correction method is applicable to other airborne LiDAR systems. Corrected intensity values can be better used for classification, especially in more refined target recognition scenarios, such as road mark extraction and forest monitoring. This study provides useful guidance for the development of future LiDAR data processing systems.

Pansharpening by Complementing Compressed Sensing with Spectral Correction

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

Spectrum Correction Using Modeled Panchromatic Image for Pansharpening

Pansharpening is a method applied for the generation of high-spatial-resolution multi-spectral (MS) images using panchromatic (PAN) and multi-spectral images. A common challenge in pansharpening is to reduce the spectral distortion caused by increasing the resolution. In this paper, we propose a method for reducing the spectral distortion based on the intensity–hue–saturation (IHS) method targeting satellite images. The IHS method improves the resolution of an RGB image by replacing the intensity of the low-resolution RGB image with that of the high-resolution PAN image. The spectral characteristics of the PAN and MS images are different, and this difference may cause spectral distortion in the pansharpened image. Although many solutions for reducing spectral distortion using a modeled spectrum have been proposed, the quality of the outcomes obtained by these approaches depends on the image dataset. In the proposed technique, we model a low-spatial-resolution PAN image according to a relative spectral response graph, and then the corrected intensity is calculated using the model and the observed dataset. Experiments were conducted on three IKONOS datasets, and the results were evaluated using some major quality metrics. This quantitative evaluation demonstrated the stability of the pansharpened images and the effectiveness of the proposed method.

CHARACTERIZATION AND CORRELATION OF THE NETWORK CHAIN DENSITY TO THE PROPERTIES OF FLUOROELASTOMER RUBBER

ABSTRACT The concentrations of triallyl isocyanurate (TAIC) in a peroxide-curable fluoroelastomer terpolymer containing 67 wt% of fluorine were varied to generate compounds of differing crosslink densities. Experimental analysis was undertaken using rheometry, hardness, stress–strain (Mooney–Rivlin), equilibrium solvent swell, and low-field nuclear magnetic resonance (NMR) using the double quantum (DQ) technique. Increasing the TAIC concentration caused a systematic rise in rheometry elastic torque, hardness, and tensile strength, whereas both elongation at break and swelling levels decreased. These results are concurrent with an enhanced overall level of crosslinking, which was confirmed by the steady increase of the Mooney–Rivlin C1 values. DQ NMR analysis using hydrogen and fluorine probes and subsequent application of fast Tikhonov regularization to the corrected intensity data were particularly useful in discerning the inhomogeneous nature of the compound morphology. The spatial distribution of the crosslink density suggests that the compound consists of small, highly crosslinked/entangled polymerized TAIC domains embedded within the elastic crosslinked matrix. A concentration of 3 phr of TAIC is optimal according to compression set testing.

Improving the use of laser scanning intensity data in complex 3D mapping of the cave environment: Case study of the Gouffre Georges Cave, France

<p>Terrestrial laser scanning (TLS) is frequently used for contactless acquiring of highly detailed and accurate three-dimensional (3D) representation of natural landscapes and man-made objects. The advantage of TLS has been exploited in mapping the underground landscapes such as caves formed in various geological settings with variable dimensions extending from narrow passage to grand domes. Highly detailed cave surveying with TLS generates millions of 3D coordinates of cave surface by which mapping of features difficult to be reached and studied directly is possible, e.g. speleothems, ceiling channels, structural rock properties and rock type alongside with the tectonic features influencing overburden stability. Besides the 3D coordinates, intensity of the backscattered laser pulse is recorded in the form of an additional attribute, influenced by various factors including spectral properties of the surface material. The studies published on the use of laser intensity have been mainly focused on the correction of intensity recorded by TLS for objects in the above-ground environment, where atmospheric attenuation, specifically humidity or dust content in the air, is negligible or it is considered constant during scanning. However, caves are specific due to their complex morphology and aerosol in their atmosphere. The presented case study focuses on these aspects in correcting the recorded intensity with a long range TLS Riegl VZ-1000 in the Gouffre Georges cave which formed on the contact of marble and lherzolite in the French Pyrenees. We present complex workflow for elimination of the influencing factors associated with the scanning geometry, including range and incidence angle, taking into account the character and contours of the cave wall surface as a set of facets and effect of atmospheric attenuation. The resulting corrected intensity value depends mostly on the spectral surface properties. Derived reflectance values revealed different lithological layers allowing to analyse their lithological and structural properties. Corrected intensity can be also used in biospeleological studies for mapping and quantification of cave fauna, in speleology for observing structures with higher occurrence of wet areas where active karst processes occur and even in archaeological studies for identification of cave paintings.</p>

Comparison of Different Feature Sets for TLS Point Cloud Classification

Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few studies investigated the potential of both intensity and color on classification using TLS point cloud. In this paper, the geometric features, color features, and intensity features were extracted based on a supervoxel neighborhood. In addition, the original intensity was also corrected for range effect, which is why the corrected intensity features were also extracted. The different combinations of these features were tested on four real-world data sets. Experimental results demonstrate that both color and intensity features can complement the geometric features to help improve the classification results. Furthermore, the combination of geometric features, color features, and corrected intensity features together achieves the highest accuracy in our test.

DAMAGE DETECTION FOR HISTORICAL ARCHITECTURES BASED ON TLS INTENSITY DATA

TLS (Terrestrial Laser Scanner) has long been preferred in the cultural heritage field for 3D documentation of historical sites thanks to its ability to acquire the geometric information without any physical contact. Besides the geometric information, most TLS systems also record the intensity information, which is considered as an important measurement of the spectral property of the scanned surface. Recent studies have shown the potential of using intensity for damage detection. However, the original intensity is affected by scanning geometry such as range and incidence angle and other factors, thus making the results less accurate. Therefore, in this paper, we present a method to detect certain damage areas using the corrected intensity data. Firstly, two data-driven models have been developed to correct the range and incidence angle effect. Then the corrected intensity is used to generate 2D intensity images for classification. After the damage areas being detected, they are re-projected to the 3D point cloud for better visual representation and further investigation. The experiment results indicate the feasibility and validity of the corrected intensity for damage detection.