scholarly journals Forman-Ricci Flow for Change Detection in Large Dynamic Data Sets

Axioms ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 26 ◽  
Author(s):  
Melanie Weber ◽  
Jürgen Jost ◽  
Emil Saucan
Keyword(s):  
Change Detection ◽  
Ricci Flow ◽  
Data Sets ◽  
Dynamic Data

scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals 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

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

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.

scholarly journals An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

2020 ◽  
Vol 12 (10) ◽  
pp. 1680
Author(s):  
Chenguang Dai ◽  
Zhenchao Zhang ◽  
Dong Lin
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Recall Rate ◽  
Data Sets ◽  
Building Extraction ◽  
Dense Matching ◽  
Object Based ◽  
Alignment Errors ◽  
Precision Rate

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

scholarly journals Metannot: A succinct data structure for compression of colors in dynamic de Bruijn graphs

2017 ◽  
Author(s):  
Harun Mustafa ◽  
André Kahles ◽  
Mikhail Karasikov ◽  
Gunnar Rätsch
Keyword(s):  
Data Structure ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Dynamic Data ◽  
De Bruijn Graphs ◽  
Dna And Rna ◽  
Succinct Data Structure ◽  
Dynamic Data Structure

AbstractMuch of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases.We propose a parallel construction method for and novel application of the wavelet trie as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data.When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes.While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes.It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices.This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.

Catching the ghost: the digital gaze of motion capture

2019 ◽  
Vol 18 (3) ◽  
pp. 305-326
Author(s):  
Vanessa Chang
Keyword(s):  
Computer Vision ◽  
Motion Capture ◽  
Individual Autonomy ◽  
Data Sets ◽  
Dynamic Data ◽  
The Uncanny ◽  
Contemporary Life ◽  
Digital Processes ◽  
Relationship Of ◽  
Virtual Body

Created with digital motion capture, or mocap, the virtual dances Ghostcatching and as.phyx.ia render movement abstracted from choreographic bodies. These depictions of gestural doubles or ‘ghosts’ trigger a sense of the uncanny rooted in mocap’s digital processes. Examining these material processes, this article argues that this digital optical uncanny precipitates from the intersubjective relationship of performer, technology, and spectator. Mocap interpolates living bodies into a technologized visual field that parses these bodies as dynamic data sets, a process by which performing bodies and digital capture technologies coalesce into the film’s virtual body. This virtual body signals a computational agency at its heart, one that choreographs the intersubjective embodiments of real and virtual dancers, and spectators. Destabilizing the human body as a locus of perception, movement, and sensation, mocap triggers uncanny uncertainty in human volition. In this way, Ghostcatching and as.phyx.ia reflect the infiltration of computer vision technologies, such as facial recognition, into numerous aspects of contemporary life. Through these works, the author hopes to show how the digital gaze of these algorithms, imperceptible to the human eye, threatens individual autonomy with automation.

An incremental space to visualize dynamic data sets

2010 ◽  
Vol 50 (3) ◽  
pp. 533-562 ◽  
Author(s):  
Roberto Dantas de Pinho ◽  
Maria Cristina Ferreira de Oliveira ◽  
Alneu de Andrade Lopes
Keyword(s):  
Data Sets ◽  
Dynamic Data
Sign in / Sign up

Export Citation Format

Share Document