A Dynamic Adaptive Framework for Remote Sensing Imagery Superpixel Segmentation and Classification via Dual-Branch Feature Learning

A Dynamic Adaptive Framework for Remote Sensing Imagery Superpixel Segmentation and Classification via Dual-Branch Feature Learning

This article presents an integrated approach for superpixel segmentation (SPS) and classification, leveraging a deep learning (DL) method tailored to high-resolution remote sensing imagery (RSI). The main contributions of this method include designing a SPS approach based on a convolution-based netw...

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Bibliographic Details
Main Authors: Wangtun Yang, Yang Zhang, Heng Zhang, Liangzhi Li
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Dual-branch feature learning
dynamic adaptive quantization
end-to-end classification
remote sensing imagery (RSI)
superpixel segmentation (SPS)
Online Access:https://ieeexplore.ieee.org/document/11068126/
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