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Code is for our paper "Change Detection from Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network," IEEE JSTARS 2019

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SAR_CD_DCNet

This code is for our paper "Change Detection from Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network". in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 11, pp. 4517-4529, Nov. 2019.

If you have any questions, please contact us. Email: [email protected] [email protected]

Before running this code, you should correctly install ubuntu system and CAFFE framework. Refer to this guildeline "http://caffe.berkeleyvision.org/installation.html" After correctly installing ubuntu and caffe, you can run this code by the following procedures.

(1) The farmland dataset (LMDB format) is prepared on "https://share.weiyun.com/5M2gyVd".

(2) Opening the terminal and running this script to execute the training of DCNet: "sh train.sh"

Then, training model named “***.caffemodel” can be obtained.

(3) Running the following script to executes the testing of DCNet and record testing logs: "sh test.sh".

(4) Final change map can be calculated by "Calculating_result.m".

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Code is for our paper "Change Detection from Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network," IEEE JSTARS 2019

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