چکیده مقاله
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 1D CNN is suggested for polarimetric feature extraction A 2D CNN is presented for spatial feature extraction and a 3D 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 1% and 5% training samples The superiority of 3D CNN compared to other methods is shown using different quantitative classification measures
کلیدواژهها
نویسندگان
Maryam Imani
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
شیوه ارجاع
Imani, Maryam,1400,Convolutional Neural Networks with Different Dimensions for POLSAR Image Classification,Fourth International Conference on Soft Computing
ارائهشده در
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