AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

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AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b2-35-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 35-42

Author(s):

A. W. Lyda ◽

X. Zhang ◽

C. L. Glennie ◽

K. Hudnut ◽

B. A. Brooks

Keyword(s):

Change Detection ◽

Point Cloud ◽

Near Field ◽

Three Dimensional ◽

Light Detection And Ranging ◽

Data Sets ◽

Light Detection ◽

Icp Algorithm ◽

Detection Algorithms ◽

Change Detection Algorithms

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Training a terrain traversability classifier for a planetary rover through simulation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417735401 ◽

2017 ◽

Vol 14 (5) ◽

pp. 172988141773540 ◽

Cited By ~ 5

Author(s):

Robert A Hewitt ◽

Alex Ellery ◽

Anton de Ruiter

Keyword(s):

Three Dimensional ◽

Point Clouds ◽

Light Detection And Ranging ◽

Data Sets ◽

Light Detection ◽

Planetary Rover ◽

Training Methodology ◽

Sensor Model ◽

Random Fashion ◽

Manual Classification

A classifier training methodology is presented for Kapvik, a micro-rover prototype. A simulated light detection and ranging scan is divided into a grid, with each cell having a variety of characteristics (such as number of points, point variance and mean height) which act as inputs to classification algorithms. The training step avoids the need for time-consuming and error-prone manual classification through the use of a simulation that provides training inputs and target outputs. This simulation generates various terrains that could be encountered by a planetary rover, including untraversable ones, in a random fashion. A sensor model for a three-dimensional light detection and ranging is used with ray tracing to generate realistic noisy three-dimensional point clouds where all points that belong to untraversable terrain are labelled explicitly. A neural network classifier and its training algorithm are presented, and the results of its output as well as other popular classifiers show high accuracy on test data sets after training. The network is then tested on outdoor data to confirm it can accurately classify real-world light detection and ranging data. The results show the network is able to identify terrain correctly, falsely classifying just 4.74% of untraversable terrain.

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Three-dimensional imaging, change detection, and stability assessment during the centerline trench levee seepage experiment using terrestrial light detection and ranging technology, Twitchell Island, California, 2012

Open-File Report ◽

10.3133/ofr20141092 ◽

2014 ◽

Cited By ~ 1

Author(s):

Gerald W. Bawden ◽

James Howle ◽

Sandra Bond ◽

Michelle Shriro ◽

Peter Buck

Keyword(s):

Change Detection ◽

Three Dimensional ◽

Light Detection And Ranging ◽

Stability Assessment ◽

Light Detection ◽

Three Dimensional Imaging

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Performance Analysis of Change Detection Algorithms on Multispectral Imagery

2020 12th International Conference on Computational Intelligence and Communication Networks (CICN) ◽

10.1109/cicn49253.2020.9242630 ◽

2020 ◽

Author(s):

Indira Bidari ◽

Satyadhyan Chickerur ◽

Rekha.M. Talikoti ◽

Smita.S. Kapali ◽

Sushmita Talawar ◽

...

Keyword(s):

Performance Analysis ◽

Change Detection ◽

Multispectral Imagery ◽

Detection Algorithms ◽

Change Detection Algorithms ◽

Analysis Of Change

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Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

Western Journal of Applied Forestry ◽

10.1093/wjaf/24.2.95 ◽

2009 ◽

Vol 24 (2) ◽

pp. 95-102 ◽

Cited By ~ 13

Author(s):

Hans-Erik Andersen

Keyword(s):

Forest Inventory ◽

Laser Scanning ◽

Three Dimensional ◽

Grid Cell ◽

Forest Stand ◽

Light Detection And Ranging ◽

Inventory Analysis ◽

Light Detection ◽

Individual Tree ◽

Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

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Change detection algorithms analysis over radar images using a generating function

10.1063/5.0027277 ◽

2020 ◽

Author(s):

Leonid G. Dorosinsky ◽

Nina S. Vinogradova

Keyword(s):

Change Detection ◽

Generating Function ◽

Detection Algorithms ◽

Radar Images ◽

Change Detection Algorithms

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An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421590497 ◽

2021 ◽

Author(s):

Gulnaz Alimjan ◽

Yiliyaer Jiaermuhamaiti ◽

Huxidan Jumahong ◽

Shuangling Zhu ◽

Pazilat Nurmamat

Keyword(s):

Change Detection ◽

Detection Algorithm ◽

Convolutional Network ◽

Detection Algorithms ◽

Multiple Dimensions ◽

Change Detection Algorithms ◽

Image Change Detection ◽

Control Loss ◽

The Relationship ◽

Entire Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

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