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Siamese-neural-network-for-change-detection (0, 2019-01-30)
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# siamese_rit_change `python3 -m pip install --index-url https://test.pypi.org/simple/ siamese-rit-change` `python3` `import siamese_rit_change` 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

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