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 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 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 %.

A novel method for change detection in spectral imagery

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