Review of ladar: a historic, yet emerging, sensor technology with rich phenomenology

McManamon, Paul

doi:10.1117/1.oe.51.6.060901

Cited by 201 publications

(79 citation statements)

References 57 publications

(54 reference statements)

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“…However, power lines stretch across long geographical distances, making it labor-intensive and costly to conduct traditional field-based inspection. In contrast, airborne LiDAR (light detection and ranging) can directly collect high-precision 3D point cloud data of the power line corridor, saving a lot of field survey time and labor [3,4]. Nevertheless, airborne LiDAR data volume is large and power lines are usually close to vegetation and buildings over urban areas.…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

et al. 2017

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Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

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Section: Introductionmentioning

confidence: 99%

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

et al. 2017

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“…A 2012 review 9 article shows E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 4 ; 3 2 6 ; 5 7 1…”

Section: No Perceptible Changementioning

confidence: 99%

Comparison of flash lidar detector options

McManamon¹,

Banks

Beck³

et al. 2017

Opt. Eng

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Abstract. Three lidar receiver technologies using the total laser energy required to perform a set of imaging tasks are compared. The tasks are combinations of two collection types (3-D mapping from near and far), two scene types (foliated and unobscured), and three types of data products (geometry only, geometry plus 3-bit intensity, and geometry plus 6-bit intensity). The receiver technologies are based on Geiger mode avalanche photodiodes (GMAPD), linear mode avalanche photodiodes (LMAPD), and optical time-of-flight lidar, which combine rapid polarization rotation of the image and dual low-bandwidth cameras to generate a 3-D image. We choose scenarios to highlight the strengths and weaknesses of various lidars. We consider HgCdTe and InGaAs variations of LMAPD cameras. The InGaAs GMAPD and the HgCdTe LMAPD cameras required the least energy to 3-D map both scenarios for bare earth, with the GMAPD taking slightly less energy. We comment on the strengths and weaknesses of each receiver technology. Six bits of intensity gray levels requires substantial energy using all camera modalities.

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“…The SNR expression of a single APD based on the traditional method is written as [21,33,35] SNR = P sig P th + P a + P dark + P shot + P back ,…”

Section: Snr Analysismentioning

confidence: 99%

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

et al. 2017

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A novel de-noising method for improving the performance of full-waveform light detection and ranging (LiDAR) based on differential optical path is proposed, and the mathematical models of this method are developed and verified. Backscattered full-waveform signal (BFWS) is detected by two avalanche photodiodes placed before and after the focus of the focusing lens. On the basis of the proposed method, some simulations are carried out and conclusions are achieved.(1) Background noise can be suppressed effectively and peak points of the BFWS are transformed into negative-going zero-crossing points as stop timing moments. (2) The relative increment percentage of the signal-to-noise ratio based on the proposed method first dramatically increases with the increase of the distance, and then the improvement gets smaller by increasing the distance. (3) The differential Gaussian fitting with the Levenberg-Marquardt algorithm is applied, and the results show that it can decompose the BFWS with high accuracy. (4) The differential distance should not be larger than c/2 × τ rmin , and two variable gain amplifiers can eliminate the inconsistency of two differential beams. The results are beneficial for designing a better performance full-waveform LiDAR.

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Review of ladar: a historic, yet emerging, sensor technology with rich phenomenology

Cited by 201 publications

References 57 publications

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Comparison of flash lidar detector options

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

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