A novel approach using a support vector machine (SVM) is proposed to classify bare earth points in LiDAR point clouds. Using graph based segmentation, the LiDAR point cloud is segmented into a set of topological components. Several features establishing relationships from those components to their neighboring components are formulated. The SVM is then trained on the segment features to establish a model for the classification of bare earth and non bare earth points. Quantitative results are presented for training and testing the proposed SVM classifier on the ISPRS data set. Using the ISPRS data set as a training set, qualitative results are presented by testing the proposed SVM classifier on data downloaded from Open Topography; which covers a variety of different landscapes and building structures in Frazier Park, California. Despite the data being captured from different sensors, and collected from scenes with different terrain types and building structures, the results shown were processed with no parameter changes. Furthermore, a confidence value is returned indicating how well the unforeseen data fits the SVM’s trained model for bare earth recognition.
We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require
three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to
intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the
more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells
and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas
where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well
paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our
algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing
the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion.
Our solution may be able to help increase the energy extracted vs. input energy.
LiDAR is an efficient optical remote sensing technology that has application in geography, forestry, and
defense. The effectiveness is often limited by signal-to-noise ratio (SNR). Geiger mode avalanche photodiode
(APD) detectors are able to operate above critical voltage, and a single photoelectron can initiate the current surge,
making the device very sensitive. These advantages come at the expense of requiring computationally intensive
noise filtering techniques. Noise is a problem which affects the imaging system and reduces the capability.
Common noise-reduction algorithms have drawbacks such as over aggressive filtering, or decimating in order to
improve quality and performance. In recent years, there has been growing interest on GPUs (Graphics Processing
Units) for their ability to perform powerful massive parallel processing. In this paper, we leverage this capability to
reduce the processing latency. The Point Spread Function (PSF) filter algorithm is a local spatial measure that has
been GPGPU accelerated. The idea is to use a kernel density estimation technique for point clustering. We
associate a local likelihood measure with every point of the input data capturing the probability that a 3D point is
true target-return photons or noise (background photons, dark-current). This process suppresses noise and allows for
detection of outliers. We apply this approach to the LiDAR noise filtering problem for which we have recognized a
speed-up factor of 30-50 times compared to traditional sequential CPU implementation.
A novel use of Felzenszwalb’s graph based efficient image segmentation algorithm* is proposed for segmenting 3D
volumetric foliage penetrating (FOPEN) Light Detection and Ranging (LiDAR) data for automated target detection. The
authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form
the graph for segmentation. Following graph formation, the angular difference in the points’ estimated normal vectors is
proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the
segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth
within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the
calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General
purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest
neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are
presented on several data sets.