Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Apr 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Semantic Prompt and Graph-Convolution-Structure Distillation Framework for Semantic Segmentation of Remote Sensing

Wujie Zhou, Jin Xie, Caie Xu

IEEE Transactions on Neural Networks and Learning Systems

|March 30, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

AESeg: Affinity-enhanced segmenter using feature class mapping knowledge distillation for efficient RGB-D semantic segmentation of indoor scenes.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

Identification of a Novel Keto Sugar Component in Streptococcus pneumoniae Serotype 12F Capsular Polysaccharide and Impact on Vaccine Immunogenicity.

Journal of immunology (Baltimore, Md. : 1950)·2023

Same author

Editorial for the Special Issue on Micro and Smart Devices and Systems.

Micromachines·2023

Same author

Incorporating Binary Metal Oxides in Poly(ethylene oxide)-Based Solid Electrolytes by Vapor Phase Infiltration.

ACS applied materials & interfaces·2023

Same author

Thermoresponsive Electrolytes for Safe Lithium-Metal Batteries.

Advanced materials (Deerfield Beach, Fla.)·2023

Same author

Protocol for quantitative nuclear magnetic resonance for deciphering electrolyte decomposition reactions in anode-free batteries.

STAR protocols·2023

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

See all related articles

We introduce SPGSNet-S*, a novel framework for high-resolution remote sensing semantic segmentation. This compact model enhances multimodal features and uses dual knowledge distillation for superior performance with reduced computational cost.

Area of Science:

Remote Sensing
Computer Vision
Deep Learning

Background:

High-resolution remote sensing semantic segmentation is vital for land-use monitoring, urban planning, and disaster response.
Current deep learning models face challenges due to modality heterogeneity, fine-scale object structures, and high computational costs.

Purpose of the Study:

To propose a compact and effective architecture for remote sensing semantic segmentation.
To address challenges of modality heterogeneity and high computational cost in deep learning models.

Main Methods:

Developed a semantic prompt and graph-convolution-structure distillation framework (SPGSNet-S*).
Integrated multimodal feature enhancement with dual-path knowledge distillation (KD).
Designed auxiliary spatial feature extraction (ASFE) and RGB representation modules for feature alignment and fusion.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Apr 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Introduced graph-convolution-based structure distillation and semantic prompt distillation (SPD).

Main Results:

SPGSNet-S* achieves competitive performance on Vaihingen and Potsdam datasets, outperforming state-of-the-art methods.
The model demonstrates high efficiency with only 8.89 M parameters and 2.29 G FLOPs.
Successfully fused noisy normalized digital surface model (nDSM) features with RGB imagery.

Conclusions:

SPGSNet-S* offers an effective and computationally efficient solution for high-resolution remote sensing semantic segmentation.
The proposed framework demonstrates the potential of integrating multimodal feature enhancement and dual knowledge distillation.
Publicly available code facilitates reproducibility and further research.