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New DDF2Pol network achieves high accuracy in PolSAR image classification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DDF2Pol, a novel dual-domain convolutional neural network designed for classifying PolSAR images. This network utilizes parallel real-valued and complex-valued streams to extract complementary information, enhanced by depth-wise convolution and a coordinate attention mechanism. Experiments show DDF2Pol achieves high accuracy on benchmark datasets, reaching 98.16% on Flevoland and 96.12% on San Francisco, with a low parameter count of 91,371. AI

RANK_REASON This is a research paper detailing a new model for image classification.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New DDF2Pol network achieves high accuracy in PolSAR image classification

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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    DDF2Pol: A Dual-Domain Feature Fusion Network for PolSAR Image Classification

    This paper presents DDF2Pol, a lightweight dual-domain convolutional neural network for PolSAR image classification. The proposed architecture integrates two parallel feature extraction streams, one real-valued and one complex-valued, designed to capture complementary spatial and…

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