Wetland change detection in Nile swamps of southern Sudan using multitemporal satellite imagery

2011 ◽  
Vol 5 (1) ◽  
pp. 053517 ◽  
Author(s):  
Ghada Soliman
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Southern Sudan ◽  
Wetland Change Detection

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

scholarly journals Thresholding Algorithm Optimization for Change Detection to Satellite Imagery

2018 ◽  
Author(s):  
René Vázquez-Jiménez ◽  
Rocío N. Ramos-Bernal ◽  
Raúl Romero-Calcerrada ◽  
Patricia Arrogante-Funes ◽  
Sulpicio Sanchez Tizapa ◽  
...  
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Algorithm Optimization ◽  
Thresholding Algorithm
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