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CNN-Enhanced Deep Sparse Representation Network for Polarimetric SAR Image Classification | IEEE Conference Publication | IEEE Xplore

CNN-Enhanced Deep Sparse Representation Network for Polarimetric SAR Image Classification


Abstract:

Deep learning networks can automatically acquire high-level semantic features for polarimetric SAR image classification, while it involves a blind learning procedure with...Show More

Abstract:

Deep learning networks can automatically acquire high-level semantic features for polarimetric SAR image classification, while it involves a blind learning procedure without explicit guidance. In contrast, sparse representation methods represent effective non-deep models with a robust mathematical mechanism serving as guidance. However, they can’t capture complex image features and semantic information. To address these issues, we propose a novel approach known as the CNN-enhanced Deep Sparse Representation Network (CE-DSRNet) for PolSAR image classification, which a Sparse Representation (SR) guided deep learning model. Initially, a sparse representation model is constructed for PolSAR images to capture essential features. Subsequently, to solve the sparse model, a Deep Sparse Representation Network (DSRNet) is devised by transforming the Soft Threshold Iterative (ISTA) optimization procedure into a network, enabling automatic learning of sparse coefficients as features. Finally, a CNN-enhanced DSRNet is introduced, integrating DSRNet with CNN to effectively extract deep semantic features and enhance classification accuracy. Experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.
Date of Conference: 07-12 July 2024
Date Added to IEEE Xplore: 05 September 2024
ISBN Information:

ISSN Information:

Conference Location: Athens, Greece

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1. INTRODUCTION

Compared to single-polarimetric SAR images, fully polarimetric SAR contains a wealth of scattering information and exhibits unique imaging characteristics. Consequently, fully polarimetric SAR finds extensive applications in military reconnaissance, forest land development, and disaster detection. PolSAR image classification, a crucial component of remote sensing image processing, has attracted significant attention from researchers. In recent decades, both traditional methods and deep learning techniques have made substantial advancements in the task of PolSAR image classification.

References

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