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المؤلفAhishali M.
المؤلفInce T.
المؤلفKiranyaz, Mustafa Serkan
المؤلفGabbouj M.
تاريخ الإتاحة2022-04-26T12:31:21Z
تاريخ النشر2019
اسم المنشورProgress in Electromagnetics Research Symposium
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/PIERS-Spring46901.2019.9017716
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082018376&doi=10.1109%2fPIERS-Spring46901.2019.9017716&partnerID=40&md5=5fc0508601d861dd8da665a7440e21ad
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30620
الملخصIn this work, we propose to use learned features for terrain classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. In the proposed classification framework, the learned features are extracted from sliding window regions using Convolutional Neural Networks (CNNs), and then they are used for the classification with the linear Support Vector Machine (SVM) classifier. The classification performance of the proposed approach is compared with numerous target decomposition theorems (TDs) as the engineered features tested with two classifiers: Collective Network of Binary Classifiers (CNBCs) and SVMs. The experimental evaluations over two commonly used benchmark AIRSAR PolSAR images, San Francisco Bay and Flevoland at L- Band, reveal that the classification performance of the learned features with CNNs outperforms the performance of the engineered features as TDs even the dimension of learned features is the quarter of the engineered features.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعBenchmarking
Convolutional neural networks
Domain decomposition methods
Photonics
Piers
Polarimeters
Radar imaging
Support vector machines
Synthetic aperture radar
Classification framework
Classification performance
Experimental evaluation
Linear Support Vector Machines
Performance comparison
Polarimetric synthetic aperture radars
Target decomposition theorems
Terrain classification
Classification (of information)
العنوانPerformance Comparison of Learned vs. Engineered Features for Polarimetric SAR Terrain Classification
النوعConference Paper
الصفحات2317-2324
رقم المجلد2019-June
dc.accessType Abstract Only


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