Robust volumetric change detection using mutual information with 3D fractals

2014 ◽  
Author(s):  
Mark Rahmes ◽  
Morris Akbari ◽  
Ronda Henning ◽  
John Pokorny
Keyword(s):  
Mutual Information ◽  
Change Detection ◽  
Volumetric Change ◽  
3D Fractals

Multicontextual Mutual Information Data for SAR Image Change Detection

2015 ◽  
Vol 12 (9) ◽  
pp. 1863-1867 ◽  
Author(s):  
Lin An ◽  
Ming Li ◽  
Peng Zhang ◽  
Yan Wu ◽  
Lu Jia ◽  
...  
Keyword(s):  
Mutual Information ◽  
Change Detection ◽  
Sar Image ◽  
Image Change Detection

A Multi-Level Approach for Change Detection of Buildings Using Satellite Imagery

2018 ◽  
Vol 27 (08) ◽  
pp. 1850031 ◽  
Author(s):  
Md. Abdul Alim Sheikh ◽  
Alok Kole ◽  
Tanmoy Maity
Keyword(s):  
Remote Sensing ◽  
Mutual Information ◽  
Change Detection ◽  
Satellite Imagery ◽  
Image Matching ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Novel Technique ◽  
Multi Level

In this paper a novel technique for building change detection from remote sensing imagery is presented. It includes two main stages: (1) Object-specific discriminative features are extracted using Morphological Building Index (MBI) to automatically detect the existence of buildings in remote sensing images. (2) Pixel-based image matching is measured on the basis of Mutual Information (MI) of the images by Normalized Mutual Information (NMI). Here, the MBI features values are computed for each of the pair images taken over the same region at two different times and then changes in these two MBI images are measured to indicate the building change. MI is estimated locally for all the pixels for image matching and then thresholding is applied for eliminating those pixels which are responsible for strong similarity. Finally, after getting the MBI and NMI images, a further fusion of these two images is done for refinement of the change result. For evaluation purpose, the experiments are carried on QuickBird, IKONOS images and images taken from Google Earth. The results show that the proposed technique can attain acceptable correctness rates above 90% with Overall Accuracy (OA) 89.52%.

scholarly journals Volumetric Change Detection with using Structure from Motion – The Impact of Repeat Station Imaging

GI_Forum ◽  
2018 ◽  
Vol 1 ◽  
pp. 135-151
Author(s):  
Andrew C. Loerch ◽  
Gernot Paulus ◽  
Christopher D. Lippitt
Keyword(s):  
Change Detection ◽  
Structure From Motion ◽  
Volumetric Change ◽  
Repeat Station ◽  
The Impact
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