Classification of polarimetric SAR images using compact convolutional neural networks
Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classif...
Main Authors: | Mete Ahishali, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2021-01-01
|
Series: | GIScience & Remote Sensing |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/15481603.2020.1853948 |
Similar Items
-
PCCN: Polarimetric Contexture Convolutional Network for PolSAR Image Super-Resolution
by: Lin-Yu Dai, et al.
Published: (2025-01-01) -
Multiscale Superpixel-Guided Weighted Graph Convolutional Network for Polarimetric SAR Image Classification
by: Ru Wang, et al.
Published: (2024-01-01) -
Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning
by: Sun Xun, et al.
Published: (2016-12-01) -
Improved General Polarimetric Model-Based Decomposition for Coherency Matrix
by: Yongzhen Li, et al.
Published: (2023-06-01) -
Polarimetric SAR Terrain Classification Using Polarimetric Features Derived from Rotation Domain
by: Tao Chensong, et al.
Published: (2017-10-01)