Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data

2019 ◽  
Author(s):  
Ying Zhang ◽  
Sylvian Leblanc
Keyword(s):  
Change Detection ◽  
Multispectral Data

scholarly journals FEASIBILITY OF MULTISPECTRAL AIRBORNE LASER SCANNING FOR LAND COVER CLASSIFICATION, ROAD MAPPING AND MAP UPDATING

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 119-122 ◽  
Author(s):  
L. Matikainen ◽  
K. Karila ◽  
J. Hyyppä ◽  
E. Puttonen ◽  
P. Litkey ◽  
...  
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Land Cover Classification ◽  
Aerial Images ◽  
Multispectral Data ◽  
Airborne Laser ◽  
First Results ◽  
Road Mapping

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015–2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data

2016 ◽  
Author(s):  
Qiangqiang Xu ◽  
Zhengjun Liu ◽  
Fangfang Li ◽  
Mingze Yang ◽  
Haicheng Ren
Keyword(s):  
Change Detection ◽  
Multispectral Data ◽  
Object Based

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

An attentional bias for LEGO® people using a change detection task: Are LEGO® people animate?

2016 ◽  
Vol 70 (3) ◽  
pp. 219-231 ◽  
Author(s):  
Mitchell R. P. LaPointe ◽  
Rachael Cullen ◽  
Bianca Baltaretu ◽  
Melissa Campos ◽  
Natalie Michalski ◽  
...  
Keyword(s):  
Change Detection ◽  
Attentional Bias ◽  
Detection Task ◽  
Change Detection Task
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