Implementation of "SIAMESE NETWORK WITH MULTI-LEVEL FEATURES FOR PATCH-BASED CHANGE DETECTION IN SATELLITE IMAGERY" [1] Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis, "Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3825. Accessed: Feb. 21, 2019.
We present a patch-based algorithm for detecting structural changes in satellite imagery using a Siamese neural network. The two channels of our Siamese network are based on the VGG16 architecture with shared weights. Changes between the target and reference images are detected with a fully connected decision network that was trained on DIRSIG simulated samples and achieved a high detection rate. Alternatively, a change detection approach based on Euclidean distance between deep convolutional features achieved very good results with minimal supervision.
Dependencies required 1)Tensorflow 2)Keras with tensorflow background 3)Numpy 4)Keras.utils 5)numpy_utils 6)Python 2.7
Data Few sample data in is present in image pairs Unzip the file Names starting with AChip has a corresponding ANeg these are the the pairs for example AChip1,ANeg1 becomes a pair AChip2.ANeg2 becomes a pair
Testing Siamese_predict.py is used for testing open command line and type python Siamese_predict.py It will ask for 1st image chip choose the image pairs as described above Do the same for 2nd image chip Output will be in command line Change or No change