PolSAR Image Classification Using Attention Based Shallow to Deep Convolutional Neural Network
- Resource Type
- Conference
- Authors
- Alkhatib, Mohammed Q.; Al-Saad, Mina; Aburaed, Nour; Zitouni, M. Sami; Al-Ahmad, Hussain
- Source
- IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :8034-8037 Jul, 2023
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Signal Processing and Analysis
Measurement
Computational modeling
Geoscience and remote sensing
Computer architecture
Streaming media
Feature extraction
Robustness
PolSAR
Complex-Valued CNN
Classification
Squeeze and Excitation Networks
- Language
- ISSN
- 2153-7003
This paper proposes a novel multi-branch feature fusion network for PolSAR image classification and interpretation. It is built using Complex-valued Convolutional Neural Networks (CV-CNNs). The proposed approach utilizes extraction of polarimetric features at each branch to achieve high classification accuracy. Moreover, Squeeze and Excitation (SE) is also introduced within the model’s architecture. SE block improves channel interdependencies with almost no additional computational cost. The proposed approach is tested and evaluated using Flevoland benchmark dataset. Experiments demonstrate the effectiveness of the proposed attention based shallow to deep CV-CNN model for PolSAR image classification in terms of Kappa Coefficient (k), Overall Accuracy (OA), and Average Accuracy (AA) metrics.