Color Restoration for Full-Waveform Multispectral LiDAR Data

Current research into the fusion of Hyperspectral Imagery (HI) and full waveform LiDAR (Light detection and ranging) has relied on first processing the full waveform LiDAR (FWL) data to a set of discrete returns before combining. However, more information about target properties can potentially be recovered if the raw waveform is preserved in the fusion with HI. This paper proposes a voxelization method to fuse raw FWL data with HI by dividing the waveform data into voxels, and then synthesizing all waveforms which intersect a voxel into one 3D superposition waveform. The efficacy of this method is evaluated by comparing the synthesized waveform with an actual nadir LiDAR waveform from the voxel of interest. Results show that this method of voxelizing and fusion of FWL data can preserve raw waveform characteristics while effectively representing the FWL data on a 3D raster basis that can be directly co-registered with the HI.

\nThe atmospheric monitoring is very important for the observation of air shower by air fluorescence technique.\nIn the Telescope Array (TA) experiment, LIDAR (LIght Detection And Ranging) system and CLF (Central Laser Facility) system have been used for the measurement of atmospheric transparency. The LIDAR system is located in the southeast of TA site. The CLF is located in the center of the TA site. The usefulness of the CLF and LIDAR systems is demonstrated by analyzing the time variation of atmospheric transparency with the systems. The two atmospheric monitor systems are complementary. Therefore, monitoring efficiency is advanced by a new LIDAR system that was installed at CLF system.\nClouds are observed with CCD camera, IR camera and eye scan visual check. In addition, we have also measured atmospheric parameters at the ground level using several weather systems.\n

Measurement and management of vegetation biomass accumulation in ecosystems typically involves extensive field data collection, which can be expensive and time consuming, while leaving the user with relatively crude inputs to intricate biomass models. Light detection and ranging (LiDAR) remote sensing, which provides extensive height measurements of terrain and vegetation, has become an effective alternative to characterize vegetation structure. In this paper, we report on ongoing efforts at developing signal processing approaches to model herbaceous biomass using a new generation of airborne laser scanners, namely full-waveform LiDAR systems. Structural and statistic-based feature metrics are directly derived from LiDAR waveforms at the pixel level and related to plot-level field data. Initial results reveal a definite correlation between the LiDAR waveform and herbaceous biomass. Ongoing research focuses on the links between fractional cover estimated from imaging spectroscopy and woody biomass.

Airborne light detection and ranging (LIDAR) system uses in many field such as military, agriculture, geology and transportation. LIDAR data acquisition consumes several gigabytes from the storage capacity of the system. So, it's necessary to reduce the on board storage capacity as well as its transmission rate. Compression of LIDAR data means reducing the amount of data required to represent the digital data acquired by LIDAR system. This is done by removing data redundancies to achieve compression. In this paper we explain LIDAR system theory and component, compare between lossy and lossless compression techniques then we discuss LIDAR data compression techniques in order to choose a suitable one for LIDAR system that gives high compression ratio with suitable processing time. In most LIDAR data compression techniques classification of LIDAR data is an important issue as a preprocessing step to simplify the data. Using more than one compression algorithms increase the compression ratio but increase the processing time as well.

The paper proposes a procedure based on Kohonen’s Self Organizing Maps (SOMs) to perform the unsupervised classification of raw full-waveform airborne LIDAR (Light Detection and Ranging) data, without the need of extracting features from them, that is without any preprocessing. The proposed algorithm allows the classification of points into three classes (“grass”, “trees” and “road”) in two subsequent stages. During the first one, all the raw data are given as input to a SOM and points belonging to the category “trees” are extracted on the basis of the number of peaks that characterize the waveforms. In the second stage, data not previously classified as “trees” are used to create a new SOM that, together with a hierarchical clustering algorithm, allows to distinguish between the classes “road” and “grass”. Experiments carried out show that raw full-waveform LIDAR data were classified with an overall accuracy of 93.9%, 92.5% and 92.9%, respectively.

The beam scanner is a predominant part in the light detection and ranging (LiDAR) system to achieve three-dimensional (3D) imaging. The solid-state beam-steering device has emerged as a promising candidate technology for a beam scanner with the advantages of robustness, stability, and high scanning speed. Here we propose a frequency modulated continuous wave (FMCW) LiDAR system with an in-fiber solid-state beam scanner. A 45° tilted fiber grating (TFG) is first employed to achieve in-fiber solid-state spectral scanning in the LiDAR system. A maximum output efficiency of 93.7% is achieved with proper polarization control. A single-mode fiber is then used to fabricate a 2-cm 45° TFG, which significantly reduces the size and the cost of the beam scanner in the LiDAR system. We experimentally realize 3D imaging of targets placed at a distance of 1.2 m based on our proposed LiDAR system. In addition, the system can achieve a detection distance of 6 m with a ranging precision of 24 mm.

Hyperspectral imagery (HSI) and light detection and ranging (LiDAR) remote sensing technologies are important ways to obtain surface information. Combining the characteristics and advantages of hyperspectral images and LiDAR DSM data, we propose a classification method for co-classification of hyperspectral images and LiDAR data in this paper. The model uses a dual-branch HSI image and LiDAR data classification method. First, the features of shallow convolution and deep convolution are merged in the spatial feature extraction process of HSI, which combines to focus on more global information. Then the space and spectrum are combined, considering the characteristics of different layers, the network has the characteristics of multiscale. Finally, the LiDAR branch introduces dilated convolution to obtain a larger receptive field and reduce information loss. The classification experimental results on Houston and Trento dataset show the superiority of our proposed method in the collaborative classification of hyperspectral images and LiDAR data.

Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction

Light Detection and Ranging (LiDAR) is one of the important techniques of remote sensing that is currently attracting considerable interest in description of vegetation structure, obtaining information about the canopy height as well as the foliage density of vegetation layer profiles. In this study, we estimate one of the important structural properties of vegetation: the profile of leaf area index (LAI). It is retrieved based on airborne full waveform LiDAR data inversion. It consists of estimating the value of LAI each time an echo is recorded. The proposed approach approximates the scattering taking into account just the first pulse collision within the vegetation cover. Moreover, to be independent from both the scattering coefficient and the pulse energy, the ratio between two successive echoes is model instead of the echo itself. The proposed algorithm is based on Bayesian modeling. Indeed, we propose a variational approach having two components: data attachment (simulated waveform is close to actual one) and regularity (the LAI value slowly varies from a position to the next). The problem is finally written as a non-linear cost function to optimize. To solve it and overcome non-linearity, we propose a new multiscale gradient technique, which start by solving simpler problem and increases progressively the complexity until converging to the original problem. For evaluating our methods, we parametrize the DART model to simulate Lidar full waveform and vegetation scenes; one with Turbid medium and the other with imported 3D olive tree. The results are promising.

Remote sensing or stand-off detection using controlled light sources is a well known and often used technique for atmospheric and surface spatial mapping. Today, ground based, vehicle-borne and airborne systems are able to cover large areas with high accuracy and good reliability. This kind of detection based on LiDAR (Light Detection and Ranging) or active Differential Optical Absorption Spectroscopy (DOAS) technologies, measures optical responses from controlled illumination of targets. Properties that can be recorded include volume back-scattering, surface reflectivity, molecular absorption, induced fluorescence and Raman scattering. The various elastic and inelastic backscattering responses allow the identification or characterization of content of the target volumes or surfaces. INO has developed instrumentations to measure distance to solid targets and monitor particles suspended in the air or in water in real time. Our full waveform LiDAR system is designed for use in numerous applications in environmental or process monitoring such as dust detection systems, aerosol (pesticide) drift monitoring, liquid level sensing or underwater bathymetric LiDARs. Our gated imaging developments are used as aids in visibility enhancement or in remote sensing spectroscopy. Furthermore, when coupled with a spectrograph having a large number of channels, the technique becomes active multispectral/hyperspectral detection or imaging allowing measurement of ultra-violet laser induced fluorescence (UV LIF), time resolved fluorescence (in the ns to ms range) as well as gated Raman spectroscopy. These latter techniques make possible the stand-off detection of bio-aerosols, drugs, explosives as well as the identification of mineral content for geological survey. This paper reviews the latest technology developments in active remote sensing at INO and presents on-going projects conducted to address future applications in environmental monitoring.

Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data

The advantages and limitations of the ZephIR®, a continuous‐wave, focused light detection and ranging (LiDAR) wind profiler, to observe offshore winds and turbulence characteristics were tested during a 6 month campaign at the transformer/platform of Horns Rev, the world's largest wind farm. The LiDAR system is a ground‐based sensing technique which avoids the use of high and costly meteorological masts. Three different inflow conditions were selected to perform LiDAR wind profiling. Comparisons of LiDAR mean wind speeds against cup anemometers from different masts showed high correlations for the open sea sectors and good agreement with their longitudinal turbulence characteristics. Cup anemometer mean wind speed profiles were extended with LiDAR profiles up to 161 m on each inflow sector. The extension resulted in a good profile match for the three surrounding masts. These extended profiles, averaged over all observed stabilities and surface roughness lengths, were compared to the logarithmic profile. The observed deviations were relatively small. Offshore wind farm wakes were also observed from LiDAR measurements where the wind speed deficits were detected at all LiDAR heights. Profile‐derived friction velocities and roughness lengths were compared to Charnock's sea roughness model. These average values were found to be close to the model, although the scatter of the individual estimations of sea roughness length was large. Copyright © 2008 John Wiley & Sons, Ltd.

Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning

Agricultural production, the main pillar of food security, is highly dependent on soil quality, and threatened by erosion processes that degrade soil quality. This article is part of a research to verify the usefulness of differential interferometric analysis on TopSAR (Terrain Observation with Progressive Scans SAR, Synthetic Aperture Radar) images to measure water and tillage erosion in small agricultural basins. For this, images from the Sentinel 1 mission are used, analyzing the deformations on the earth’s surface. The purpose of this research is to verify the accuracy of the proposed method by comparing its measures with the ones taken with the gold standard laser terrestrial LIDAR (Light Detection and Ranging) system, as well as to establish a basic step period framework that guarantees an admissible loss of coherence. The results on a pilot plot in El Molar (north of Madrid, Spain) showed that the differences lay within the range of the error associated with the very LIDAR system and showed that coherence losses correspond with the deformations measured. Given the economic and labor advantages of the differential interferometric analysis, this method could be regarded as an excellent alternative to the use of LIDAR in large-scale studies for measuring ground deformation caused by water and tillage erosion.

<p>LiDAR (Light Detection and Ranging) is a technology that provides three-dimensional point cloud data with high spatial accuracy. It is increasingly used in various applications and disciplines, including engineering. Along with other capabilities, LiDAR systems are able to record reflected backscattered energy as intensity data and measured distances to targets as range data. As the laser signal wavelength that operates in LiDAR sensors is typically in the near-infrared (NIR) spectrum, high spectral reflectance separability can be observed and a number of different materials distinguished. These capabilities have motivated researchers to study the applicability of using LiDAR range and intensity data for pavement crack extraction.</p> <p>The main goal of the present research is to examine the potential use of LiDAR range and intensity data for automated pavement crack detection. The study explores the following: a) crack detection using the two image classification logics of pixel-based and object-based image classification; b) crack detection using the Maximum Likelihood Classifier (MLC), the Random Trees Classifier (RTC), and the Support Vector Machine (SVM) classifier; c) crack detection by conducting image classification of four scenarios of multi-layer images generated from LiDAR data; and d) crack detection using LiDAR data with different point spacing.</p> <p>The experimental results indicate that the use of LiDAR data independently is effective for pavement crack detection. In addition, the results show that object-based image classification logic optimizes classification accuracy and crack detection results, and that employing SVM enhances the classification process and improves crack detection accuracy. It is also observed that using range data separately produces high accuracy for the crack detection results while the accuracy is slightly reduced when intensity data are combined with range data compared with using range data only. Furthermore, in the classification process, adding slope and aspect layers to range and intensity data decreases the accuracy of crack detection results, while increasing LiDAR data point spacing reduces crack extraction accuracy.</p>