Convolutional Neural Networks with Different Dimensions for POLSAR Image Classification
Publish place: Fourth International Conference on Soft Computing
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG04_123
تاریخ نمایه سازی: 23 اسفند 1400
Abstract:
Polarimetric Synthetic aperture radar (PolSAR) images contain polarimetric and spatial information of materials present in the scene. Three simple architectures of convolutional neural networks (CNNs) with different dimensions are proposed for PolSAR image classification in this work. A one dimensional CNN (۱D CNN) is suggested for polarimetric feature extraction. A ۲D CNN is presented for spatial feature extraction and a ۳D CNN is introduced for polarimetric-spatial feature extraction. The performance of CNNs are compared with morphological profile of PolSAR cube when fed to the support vector machine (SVM) and random forest (RF) classifiers. The experiments are done in two cases of using ۱% and ۵% training samples. The superiority of ۳D CNN compared to other methods is shown using different quantitative classification measures.
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Authors
Maryam Imani
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran