A novel method for change detection in spectral imagery

2010 ◽  
Author(s):  
Ariel Schlamm ◽  
David Messinger ◽  
William Basener
Keyword(s):  
Change Detection ◽  
Novel Method

scholarly journals A Novel Method of Unsupervised Change Detection Using Multi-Temporal PolSAR Images

2017 ◽  
Vol 9 (11) ◽  
pp. 1135 ◽  
Author(s):  
Wensong Liu ◽  
Jie Yang ◽  
Jinqi Zhao ◽  
Le Yang
Keyword(s):  
Change Detection ◽  
Multi Temporal ◽  
Novel Method

scholarly journals A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure

2017 ◽  
Vol 9 (8) ◽  
pp. 846 ◽  
Author(s):  
Jinqi Zhao ◽  
Jie Yang ◽  
Zhong Lu ◽  
Pingxiang Li ◽  
Wensong Liu ◽  
...  
Keyword(s):  
Change Detection ◽  
Similarity Measure ◽  
Novel Method ◽  
Joint Classification

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

scholarly journals A METHOD OF TIME-SERIES CHANGE DETECTION USING FULL POLSAR IMAGES FROM DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 1127-1133
Author(s):  
W. Liu ◽  
J. Yang ◽  
J. Zhao ◽  
H. Shi ◽  
L. Yang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Detection Methods ◽  
Series Data ◽  
Difference Image ◽  
Novel Method ◽  
Difference Images ◽  
The City

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by R<sub>j</sub> statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

scholarly journals FLOOR IDENTIFICATION WITH COMMERCIAL SMARTPHONES IN WIFI-BASED INDOOR LOCALIZATION SYSTEM

2016 ◽  
Vol XLI-B4 ◽  
pp. 573-577
Author(s):  
H. J. Ai ◽  
M. Y. Liu ◽  
Y. M. Shi ◽  
J. Q. Zhao
Keyword(s):  
Change Detection ◽  
Bp Neural Network ◽  
Indoor Localization ◽  
Sensor Data ◽  
Smart Devices ◽  
Motion Pattern ◽  
Improve Performance ◽  
K Nearest Neighbors ◽  
Mobile Client ◽  
Novel Method

In this paper, we utilize novel sensors built-in commercial smart devices to propose a schema which can identify floors with high accuracy and efficiency. This schema can be divided into two modules: floor identifying and floor change detection. Floor identifying module starts at initial phase of positioning, and responsible for determining which floor the positioning start. We have estimated two methods to identify initial floor based on K-Nearest Neighbors (KNN) and BP Neural Network, respectively. In order to improve performance of KNN algorithm, we proposed a novel method based on weighting signal strength, which can identify floors robust and quickly. Floor change detection module turns on after entering into continues positioning procedure. In this module, sensors (such as accelerometer and barometer) of smart devices are used to determine whether the user is going up and down stairs or taking an elevator. This method has fused different kinds of sensor data and can adapt various motion pattern of users. We conduct our experiment with mobile client on Android Phone (Nexus 5) at a four-floors building with an open area between the second and third floor. The results demonstrate that our scheme can achieve an accuracy of 99% to identify floor and 97% to detecting floor changes as a whole.

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