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صفحه اصلی
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سی و یکمین کنفرانس بین المللی مهندسی برق
Unsupervised Change Detection in SAR Images Using a Six-Branch CNN and Adaptive Window Approach
نویسندگان :
Abbas Kakoolvand
1
Maryam Imani
2
Hassan Ghassemian
3
1- دانشگاه تربیت مدرس
2- دانشگاه تربیت مدرس
3- دانشگاه تربیت مدرس
کلمات کلیدی :
Neural Network،Change Detection (CD)،Remote Sensing،Synthetic Aperture Radar (SAR)،Convolutional Neural Network (CNN)
چکیده :
Change detection is one of the important and hot topics in remote sensing. Adaptive windowing approaches can preserve image details while reduce noise in the process of change detection. In the proposed method, two difference images (DIs) are obtained by using the adaptive window approach. Some fake labels are provided from these DIs. Neural networks have a good performance in image processing. To use this advantage, a six-branch convolutional neural network (CNN) is trained using the fake labels. Using these six branches with utilizing the adaptive windows, the network can preserve image detail while reduce noise. The proposed method based on three criteria (PCC, Kappa, and F1 score) has the best results in two datasets and has results close to the best method in other datasets.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.6.0
